{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "pd.set_option('display.max_columns', None) # show all columns\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import statsmodels.api as sm\n",
    "import helperFunctions\n",
    "import os\n",
    "from math import ceil\n",
    "from collections import Counter\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "from sklearn.preprocessing import label_binarize\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "from tqdm import tqdm, tqdm_notebook\n",
    "tqdm.pandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read the train and test csv files\n",
    "og_train_df = pd.read_csv('feature_engineered_data/train.csv')\n",
    "og_test_df = pd.read_csv('feature_engineered_data/test-3.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Mapping dictionary\n",
    "# mapping = {'H': 1, 'D': 0, 'A': 2}\n",
    "\n",
    "# # Replace values in the FTR column\n",
    "# og_train_df['FTR'] = og_train_df['FTR'].map(mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of null values in og_train_df = 0\n",
      "Number of null values in og_test_df = 0\n"
     ]
    }
   ],
   "source": [
    "print(f\"Number of null values in og_train_df = {og_train_df.isnull().sum().sum()}\")\n",
    "print(f\"Number of null values in og_test_df = {og_test_df.isnull().sum().sum()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[0;31mSignature:\u001b[0m  \u001b[0mhelperFunctions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmaxDstDrw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mDocstring:\u001b[0m\n",
      "Returns max distance-from-drawn-match value based on betting odds captured by Bet365.\n",
      "Positive values mean homeTeam has that much more than chances of winning instead of a draw.\n",
      "Negative values means awayTeam has that much more than chances of winning instead of a draw. Negative value here does not less chances (the absolute value must be taken) but signals an awayTeam winning chances over a draw.\n",
      "\u001b[0;31mFile:\u001b[0m      ~/Documents/Projects/model_todo/helperFunctions.py\n",
      "\u001b[0;31mType:\u001b[0m      function"
     ]
    }
   ],
   "source": [
    "? helperFunctions.maxDstDrw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 375x375 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# dist. of the target variable in og_train_df\n",
    "plt.figure(figsize=(3, 3), dpi=125)\n",
    "plt.subplot(1, 1, 1)\n",
    "sns.countplot(x='FTR', data = og_train_df)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([29.7  , 25.655, 44.644])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.round(np.array(list(Counter(og_train_df[\"FTR\"]).values()))/len(og_train_df[\"FTR\"])*100, 3)  # percentage distribution of homeWins, draws, awayWins in the og_train_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows in training split = 2136\n",
      "Number of rows in validation split = 534\n"
     ]
    }
   ],
   "source": [
    "# assuming 80-20 split for train and validation, we will get roughly these many rows in the two splits\n",
    "\n",
    "print(f\"Number of rows in training split = {ceil(len(og_train_df)*0.8)}\")\n",
    "print(f\"Number of rows in validation split = {len(og_train_df) - ceil(len(og_train_df)*0.8)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df, vld_df = train_test_split(og_train_df, test_size=0.2, random_state=0, stratify=og_train_df[\"FTR\"]) # we will be setting random_state=0 everytime we break og_train_df into train_df and vld_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 750x375 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# dist. of the target variable\n",
    "plt.figure(figsize=(6, 3), dpi=125)\n",
    "plt.subplot(1, 2, 1)\n",
    "sns.countplot(x='FTR', data = train_df, order=['H', 'D', 'A'])\n",
    "plt.title(\"Training data\")\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "sns.countplot(x='FTR', data = vld_df, order=['H', 'D', 'A'])\n",
    "plt.title(\"Validation data\")\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([44.663, 25.655, 29.682])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.round((np.array([tup[1] for tup in sorted(Counter(train_df[\"FTR\"]).items(), reverse=True)])/len(train_df))*100, 3) # H, D, A distribution percentages in train_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([44.569, 25.655, 29.775])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.round((np.array([tup[1] for tup in sorted(Counter(vld_df[\"FTR\"]).items(), reverse=True)])/len(vld_df))*100, 3) # H, D, A distribution percentages in vld_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_X, train_yTrue = train_df.iloc[:, :-1], train_df.iloc[:, -1]\n",
    "vld_X, vld_yTrue = vld_df.iloc[:, :-1], vld_df.iloc[:, -1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid = {\n",
    "    'n_estimators': [75, 200, 500, 1000],\n",
    "    'criterion': ['gini', 'entropy'],\n",
    "    'max_depth': [1, 3, 5, None], \n",
    "    'min_samples_split': [0.05, 0.1, 0.15],\n",
    "    'min_samples_leaf': [0.05, 0.1, 0.15],\n",
    "    'max_features': ['sqrt', 5, 8]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "gridSearcher = GridSearchCV(RandomForestClassifier(class_weight='balanced'), param_grid=param_grid, scoring='precision_macro', verbose=1, n_jobs=-1, cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 864 candidates, totalling 4320 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sigurd/mambaforge/envs/py311/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1469: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/sigurd/mambaforge/envs/py311/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1469: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/sigurd/mambaforge/envs/py311/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1469: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/sigurd/mambaforge/envs/py311/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1469: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/sigurd/mambaforge/envs/py311/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1469: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/sigurd/mambaforge/envs/py311/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1469: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/sigurd/mambaforge/envs/py311/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1469: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/sigurd/mambaforge/envs/py311/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1469: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/sigurd/mambaforge/envs/py311/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1469: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/sigurd/mambaforge/envs/py311/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1469: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/sigurd/mambaforge/envs/py311/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1469: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/sigurd/mambaforge/envs/py311/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1469: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/sigurd/mambaforge/envs/py311/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1469: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/sigurd/mambaforge/envs/py311/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1469: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/sigurd/mambaforge/envs/py311/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1469: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/sigurd/mambaforge/envs/py311/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1469: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/sigurd/mambaforge/envs/py311/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1469: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/sigurd/mambaforge/envs/py311/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1469: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/sigurd/mambaforge/envs/py311/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1469: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/sigurd/mambaforge/envs/py311/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1469: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/sigurd/mambaforge/envs/py311/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1469: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/sigurd/mambaforge/envs/py311/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1469: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/sigurd/mambaforge/envs/py311/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1469: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/sigurd/mambaforge/envs/py311/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1469: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/sigurd/mambaforge/envs/py311/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1469: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=RandomForestClassifier(class_weight=&#x27;balanced&#x27;),\n",
       "             n_jobs=-1,\n",
       "             param_grid={&#x27;criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],\n",
       "                         &#x27;max_depth&#x27;: [1, 3, 5, None],\n",
       "                         &#x27;max_features&#x27;: [&#x27;sqrt&#x27;, 5, 8],\n",
       "                         &#x27;min_samples_leaf&#x27;: [0.05, 0.1, 0.15],\n",
       "                         &#x27;min_samples_split&#x27;: [0.05, 0.1, 0.15],\n",
       "                         &#x27;n_estimators&#x27;: [75, 200, 500, 1000]},\n",
       "             scoring=&#x27;precision_macro&#x27;, verbose=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=RandomForestClassifier(class_weight=&#x27;balanced&#x27;),\n",
       "             n_jobs=-1,\n",
       "             param_grid={&#x27;criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],\n",
       "                         &#x27;max_depth&#x27;: [1, 3, 5, None],\n",
       "                         &#x27;max_features&#x27;: [&#x27;sqrt&#x27;, 5, 8],\n",
       "                         &#x27;min_samples_leaf&#x27;: [0.05, 0.1, 0.15],\n",
       "                         &#x27;min_samples_split&#x27;: [0.05, 0.1, 0.15],\n",
       "                         &#x27;n_estimators&#x27;: [75, 200, 500, 1000]},\n",
       "             scoring=&#x27;precision_macro&#x27;, verbose=1)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(class_weight=&#x27;balanced&#x27;)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(class_weight=&#x27;balanced&#x27;)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5, estimator=RandomForestClassifier(class_weight='balanced'),\n",
       "             n_jobs=-1,\n",
       "             param_grid={'criterion': ['gini', 'entropy'],\n",
       "                         'max_depth': [1, 3, 5, None],\n",
       "                         'max_features': ['sqrt', 5, 8],\n",
       "                         'min_samples_leaf': [0.05, 0.1, 0.15],\n",
       "                         'min_samples_split': [0.05, 0.1, 0.15],\n",
       "                         'n_estimators': [75, 200, 500, 1000]},\n",
       "             scoring='precision_macro', verbose=1)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gridSearcher.fit(train_X, train_yTrue)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6092712440757977"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gridSearcher.best_score_ # mean cross-validated precision_macro score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(class_weight=&#x27;balanced&#x27;, max_depth=3, max_features=5,\n",
       "                       min_samples_leaf=0.05, min_samples_split=0.1,\n",
       "                       n_estimators=200)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(class_weight=&#x27;balanced&#x27;, max_depth=3, max_features=5,\n",
       "                       min_samples_leaf=0.05, min_samples_split=0.1,\n",
       "                       n_estimators=200)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(class_weight='balanced', max_depth=3, max_features=5,\n",
       "                       min_samples_leaf=0.05, min_samples_split=0.1,\n",
       "                       n_estimators=200)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gridSearcher.best_estimator_ # we will make use of this model to predict our vld_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "vld_yPred = gridSearcher.predict(vld_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['A', 'D', 'H'], dtype=object)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gridSearcher.classes_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "vld_confMat = confusion_matrix(y_true=vld_yTrue, y_pred=vld_yPred, labels=gridSearcher.classes_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[106,  45,   8],\n",
       "       [ 34,  75,  28],\n",
       "       [ 15,  68, 155]])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vld_confMat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.667, 0.283, 0.05 ],\n",
       "       [0.248, 0.547, 0.204],\n",
       "       [0.063, 0.286, 0.651]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "helperFunctions.normalizeConfusionMatrix(vld_confMat) # get normalized CM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Accuracy = 62.9%'"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "helperFunctions.calculate_accuracy(vld_confMat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           A       0.68      0.67      0.68       159\n",
      "           D       0.40      0.55      0.46       137\n",
      "           H       0.81      0.65      0.72       238\n",
      "\n",
      "    accuracy                           0.63       534\n",
      "   macro avg       0.63      0.62      0.62       534\n",
      "weighted avg       0.67      0.63      0.64       534\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_true=vld_yTrue, y_pred=vld_yPred, labels=gridSearcher.classes_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['A', 'D', 'H'], dtype=object)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gridSearcher.classes_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "812     A\n",
       "193     H\n",
       "214     H\n",
       "1718    H\n",
       "2399    D\n",
       "       ..\n",
       "459     A\n",
       "633     D\n",
       "1407    H\n",
       "195     H\n",
       "1900    A\n",
       "Name: FTR, Length: 534, dtype: object"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vld_yTrue # we have this"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 0, 0],\n",
       "       [0, 0, 1],\n",
       "       [0, 0, 1],\n",
       "       ...,\n",
       "       [0, 0, 1],\n",
       "       [0, 0, 1],\n",
       "       [1, 0, 0]])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vld_yTrue_scores = label_binarize(vld_yTrue, classes=gridSearcher.classes_) # we binarize the validation labels\n",
    "vld_yTrue_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.51209317, 0.33219033, 0.1557165 ],\n",
       "       [0.3258604 , 0.42459936, 0.24954024],\n",
       "       [0.41136391, 0.4090233 , 0.17961279],\n",
       "       ...,\n",
       "       [0.11874371, 0.26235804, 0.61889825],\n",
       "       [0.02602337, 0.12198259, 0.85199403],\n",
       "       [0.51756921, 0.3324804 , 0.14995039]])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vld_yPred_scores = gridSearcher.predict_proba(vld_X)\n",
    "vld_yPred_scores # this shows us the probability predictions for each by the best_estimator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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PH+f9999P0+OT3jggRVGy8Y4sk9611YzpaU//GyV//8aMGZPudOoKFSoAhvFFly9fZt26dWzfvp0FCxbw7bffMnfu3HRrgCQLCAjg5MmTbNu2jS1btrBlyxZ++uknBgwYkGawbUY6d+6Ms7MzAwcOJD4+3qTHKbfp9XoCAgJYunSp2f1PJ6LCtkgyInIseQBa8sC55IGLjRo1wsfHh19//ZWPPvrI7C/9xYsXA9CxY0fjOUWKFGHZsmV8+OGH2RrEWr58ebZt20Z4eHi6vSPJgzCfHuRobmZMZnr37s3w4cM5f/48K1aswM3NjU6dOpnEA4Zen+QPd2soXbo058+fT9OefGvEXPd4Trz++ut8++23fPzxx7z00kvGaqgPHz5kzZo1NGnSxHhsVgd/mlO6dGlOnz6NoigmSePT7zX5/Z0/f54WLVqY7Dt//rzV3392lC5dmn///Re9Xm/SO5LVf6Ny5coBhls7WfnZ8fX1ZfDgwQwePJjo6GiaNGnCp59+akxGMrpN6eTkRKdOnejUqRN6vZ7hw4czb948xo8fb0x4MuPq6krXrl355ZdfjAULzcnqv13yV3O3np7+eShfvjw7d+6kYcOGNlvnRaRPbtMIq2jWrBn16tVjxowZxuJGbm5ujBkzhvPnz/PRRx+lOWfTpk0sWrSINm3a8MILLxjPef/99zl79izvv/++2b9Of/nllwxnoHTv3h1FUZg4cWKafcmv5+XlhZ+fH/v37zfZ/91332X9Tae6nr29PcuWLWPVqlV07NgRd3d34/46depQvnx5vvnmG+NMg9QePHhg8TXB0Fvx119/ceTIEWNbTEwMP/zwA2XKlElzTz2nHBwc+N///sfZs2dZt24dkNKrkPrfKSEhIVvfx2Tt27fnzp07rF692tim1Wr54YcfTI6rW7cuAQEBzJ0712Ta75YtWzh79iwdOnTIdgzW0r59e+7du2e8jQeGWSazZs3Cw8ODpk2bZnh+QEAAzZo1Y968edy9ezfN/tQ/Ow8fPjTZ5+HhQYUKFUy+N8k/l08n4U+fa2dnR40aNQDSnVKdnjFjxjBhwgTGjx+f7jFZ/bcLCgqiVq1a/Pzzzya3/Hbs2JFmTFSvXr3Q6XR8/vnnaa6XlJSU5j0L2yI9I8Jq3nvvPXr27MmiRYuMgz0/+OADTpw4wZQpUzhy5Ajdu3fH1dWVgwcP8ssvv1ClSpU03cDvvfceZ86cYdq0aezZs4cePXoQGBjIvXv3WLt2LX/99ReHDx9ON47mzZvTv39//u///o+LFy/Stm1b9Ho9Bw4coHnz5rz11luAYTDjV199xauvvkrdunXZv38/Fy5csPh9BwQE0Lx5c6ZPn87jx4/p3bu3yX47OzsWLFhAu3btePbZZxk8eDDBwcHcvn2bPXv24OXlxYYNGyy+7gcffMCyZcto164db7/9Nr6+vvz8889cvXqV3377Lc04BWsYNGgQn3zyCVOmTKFr1640aNCAIkWKMHDgQN5++200Gg1LlizJ0S2OYcOGMXv2bAYMGMCxY8cICgpiyZIluLm5mRzn6OjIlClTGDx4ME2bNqVPnz6EhoYyc+ZMypQpw7vvvpvTt5tjr732GvPmzWPQoEEcO3aMMmXKsHr1ag4dOsSMGTOyNFB7zpw5NGrUiOrVqzNs2DDKlStHaGgoR44c4datW/zzzz+A4VZns2bNqFOnDr6+vvz999+sXr3a+PMOhsQY4O2336ZNmzbY29vz8ssv8+qrrxIeHk6LFi0oUaIE169fZ9asWdSqVct4uzWratasSc2aNTM8xpJ/u8mTJ9OhQwcaNWrEkCFDCA8PZ9asWTz77LMmyX3Tpk15/fXXmTx5MidPnqR169Y4Ojpy8eJFVq1axcyZM00GRQsbo9Y0HpE/pVeBVVEURafTKeXLl1fKly9vMkVQp9MpP/30k9KwYUPFy8tLcXFxUZ599lll4sSJGVYyXb16tdK6dWvF19dXcXBwUIKCgpTevXsre/fuzTTOpKQkZerUqUrlypUVJycnxd/fX2nXrp1y7Ngx4zFarVYZOnSo4u3trXh6eiq9evVS7t+/n+7U3oyqR86fP18BFE9PT5OpiqmdOHFC6datm1K0aFHF2dlZKV26tNKrVy9l165dmb4f0qn0efnyZaVHjx6Kj4+P4uLiotSrV0/ZuHGjyTHJU3tXrVqV6XUyu56iKMqnn35qMsX20KFDygsvvKC4uroqxYsXV8aOHats27YtTbXUpk2bKs8++2ya1zNXSfP69etK586dFTc3N8XPz0955513jFM0n67AumLFCqV27dqKs7Oz4uvrq/Tr10+5detWmmu4u7unuXZ6MZUuXTrTabKKkrUKrKGhocrgwYMVPz8/xcnJSalevXqaKeXJU3ufnjad7PLly8qAAQOUwMBAxdHRUQkODlY6duyorF692njMF198odSrV0/x8fFRXF1dlcqVKytffvmlkpCQYDwmKSlJGTlypOLv769oNBrjNN/k/2sBAQGKk5OTUqpUKeX1119X7t69a5XvQXr/h7Lyb6coivLbb78pVapUUZydnZWqVasqa9asSbcC6w8//KDUqVNHcXV1VTw9PZXq1asrY8eOVe7cuWM8Rqb22h6NoqgwSksIIYQQ4gkZMyKEEEIIVUkyIoQQQghVSTIihBBCCFVJMiKEEEIIVUkyIoQQQghVSTIihBBCCFUVuqJner2eO3fu4OnpmaMVXIUQQojCRlEUHj9+TPHixa1aWLHQJSN37tyhZMmSaochhBBC5Fs3b96kRIkSVnu9QpeMJJdfvnnzpnGJdyGEEEJkLioqipIlS2ZpKQNLFLpkJPnWjJeXlyQjQgghRDZYe5iDDGAVQgghhKokGRFCCCGEqiQZEUIIIYSqJBkRQgghhKokGRFCCCGEqiQZEUIIIYSqJBkRQgghhKokGRFCCCGEqiQZEUIIIYSqJBkRQgghhKokGRFCCCGEqiQZEUIIIYSqJBkRQgghhKokGRFCCCGEqiQZEUIIIYSqVE1G9u/fT6dOnShevDgajYa1a9dmes7evXt57rnncHZ2pkKFCixatCjX4xRCCCFUo48HXVTah6LPwrkxac/TR2d+nqKYv6Y+LufvxwyHXHnVLIqJiaFmzZoMGTKEbt26ZXr81atX6dChA2+88QZLly5l165dvPrqqwQFBdGmTZs8iFgIIYTIAUWBxEuQdD+lzaUm2Hmkf86j/4MHY9O2V7gDDkEZX+9WF9DuMm1zrg5l/80kUD1c9E7b7Dgyk/OyR9VkpF27drRr1y7Lx8+dO5eyZcsybdo0AKpUqcLBgwf59ttvJRkRQogCSlFAq83FFz98GMLCsne+PhESLoDuLjjXBAf/9I/VRcC94Wnb/b8Ep7Lpnxd9HiLd0rYHbgd7HwCSFB37Yv4jWh9rekzULUh66ly7SPD5KP3rgeH78ijtNbUczfi8bFI1GbHUkSNHCAkJMWlr06YNo0aNSvec+Ph44uPjjc+joqJyKzwhhBBWpijQqJEhX8gdGqBhbr24GQOzcU4XYEEWjjN3h+HLbFwv2SQzbVGAmR6THMpXyci9e/coVqyYSVuxYsWIiooiNjYWV1fXNOdMnjyZiRMn5lWIQgghMmFJT0dMTG4mIsJW5KtkJDvGjRvH6NGjjc+joqIoWbKkihEJIYT15eqtDCtSFGjcGE6etPzc0PMRuC+ZC5GRaXcm3YPHa0FJSGlzeR7cXjT/YnotRPwEV/zhyD1oDnzzZF/ATCjyasbBhH8HD94zbSu+EhyLozjX4l70PfRPDzDVPYKIBRA5/6nz1oBLtfSvFTEfwqembS91EBwCAJh6eCoLjs9nZL23mRwyOeWYmx1Bu8f0POdqUObPjN+fooMLXgCcOKvnh5WJ/N+HTsS6vknxmhmfmh35KhkJDAwkNDTUpC00NBQvLy+zvSIAzs7OODs750V4Qgihity/laG+hvVv479pFZovxllw1r4nj4w8NnzxBpKHSLgngXsmp8UnQcxT2Z/yG8Tc4I0jFZh3bJ4Fcba14NjUnjN96gSOLgm4p449qBck1DU9zqFY5u9P0UDJ9zjwx3U6vPErj6N1VKhcn1GjGgHTsxlv+vJVMvLiiy+yefNmk7YdO3bw4ovpZL5CCFFAZNTzkR9vZdSqBQcOgEZjZufjzXCnp0mTW+lJaL5/MiW1Zk1o+9QHeOJtiPrFtM21Abg2hLi/wPUF032KYrh4/DlIWGd+uIWlIn+CgJn8cWshAPYae+w0T1fQ0AHJPSYawP7J15zzcPKgTYWnJnN4D8jei2ns2Hb8RV7q/TWxsQk0bdqUdz9ej6IoOQ/UDFWTkejoaC5dumR8fvXqVU6ePImvry+lSpVi3Lhx3L59m8WLFwPwxhtvMHv2bMaOHcuQIUPYvXs3K1euZNOmTWq9BSGEyHWW9HyEhmL6l3Fu27ABZsyApCSLTnOzi0PTMp2dughIfCrzcpgOoTrDdv368NVXpvu1h+HGU8lI0WaGJEXxg+Cnjk8WtRrurEvV4AhpEggzNPagcUp5ruig6FgoMhIwJCNb+m2hVflWpuclPQB9BDiWAY1j5tdRyZo1a3j55ZdJTEykXbt2/Pbbb7i6uubaJBBVk5G///6b5s2bG58nj+0YOHAgixYt4u7du9y4ccO4v2zZsmzatIl3332XmTNnUqJECRYsWCDTeoUQBZpWm7VEpGFD8PdPp7cht3z/Dfy5Pw8ulPJZQFbH/T2cBBo3KLk9/WPsixh6T1wbQdFxYJ/FmSJFRrDmXhBjto8hXpc8Y/Nn4Gfux9xP/zwHfyCD6b82YMmSJQwePBidTkfPnj355ZdfcHJyyvzEHNAoudXnYqOioqLw9vYmMjISLy8vtcMRQohMxcSAx5OaWOn2fOh0uLkqeZuIADRtasiUxo2DBg2ydk7MLni8BtBA4Oy0+7V/wcPPTds82kHsH+DXHjovAoen/pZOuGhIPlJzKAVePQ0DNnNBz1U9Wf3farP7HOwcuPDWBcoWyaB+iA168OAB5cqVIzo6msGDBzN//nzs7e2N+3PrMzRfjRkRQojCJHmcSExMSpu7u5lkZNIk+PhjwwlqqVsXOnbM+Bh9PIS+BZFPamY4VYFyZs55rMDtpxu3gFsLKDEP7Mx8dDlVhKCfshN5tiX/Lf9Bww/oXa23yb7insUJcA/I03iswd/fn99//52tW7fy9ddfY2eXN6vGSDIihBA2yKIZMps3q5uIeHsbRqRmRv84JREBjMujKQmm4y9cX0h7a8WxHDiVz2mk2fY4/jFjd4zlXsw9Y9uftwzTY0t6l6RWYC2VIss5RVG4c+cOwcHBAISEhKQpMJrbJBkRQggbZG6cSMOG4GamKrjRzz9Dp065GpdZbm6Q3RIKusdwbwgEr0ppc/AHh1bpn6OCrZe2MvfYXLP78mMPSDK9Xs+oUaNYtmwZBw4coHLlyqrEIcmIEELYuORxIm5umQxO9fCAIkXyLC7AcOuFRMMaLalltPCb8dzHcKudoWCZjUvQGYqpVfGrwqgXRhnb/d386fSMCgmgFSQlJTFs2DAWLVoEwB9//CHJiBBCFCaZVUzNdJxIblB0EH8alFSBOdcCO/NFJQHDoNGHn5m2BXwLvqMyv17SDcPDO5NqpzakhFcJXqvzmtph5FhCQgL9+vVj9erV2Nvb89NPP9G/f3/V4pFkRAgh8liOK6ZGR8OiRRARYXieqgRCtmkPwY1GadvLngPnZ7L+OllNRJK5NoYAM6XORa7RarX06NGDLVu24OjoyIoVK3jppZdUjUmSESGEyGNZrRsC6YwTWbQIRo5Me3CGA0oyoOjgthU+jNyag0MQRK1IabMvBu7Nnmx7Q+lUa6I4FAfHEjm/rsiyqKgoOnXqxP79+3F1deX333+3iVpdkowIIYSKMquYanacSHKPSOXKhlXnAIKDoUWL7AUR/x+4tTQkCxGWrKnyFO2etIuyFRmdkoxoHMG1XvZfP5ck6BL449YfJOoS0z3m9P3TeRhR7rG3tycpKQkvLy82bdpEo0ZmesNUIMmIEELkhm++gV27zO9LcgbWAuDeryvuDvHmj0tP8jIaTZrAvAySB0WB+H/h/iiIOw4u9aDEWrB7KvtxLAXFl0DoKMviyIxDqSfl0W3bW5vfYv7x+ZkfCGbWmslf3N3d2bRpE9evX6dmzVxYfjebJBkRQghri41FeW8sWswP/IxJvWTqzh1ABiNZMxIYmPkx12oZvjoEQfDKtIkIZL0EujluTYHxadudKoB7e3Dwy/5r55FrEdcAw+BUHxefdI9ztHPkjbpv5E1QVnT9+nU2btzIiBEjAPDx8cHHx0fdoJ4iyYgQQliZotPTiAMcpmHmB/8wH5wtW2QOMNy/ad8+42MSr6RsuzaFBx89eaID7CHgG7BLZ5yJ96vg3towriMj7i0MDxsRkxCDXtFnfmAqSXrD9/+rll/Rr0a/3AhLNRcuXCAkJISbN2/i5OTEsGHD1A7JLElGhBAF344d0L+/6XzZXKAooMWNGMWNw1zN9PiGDcHt1b7WWkHejFQv/Hi56S6H4lBsjmmb38fgOxocy+bxanvW8dGuj5h0cFLmBxYS//77L61ateL+/ftUrlyZ9pklryqSZEQIUfBt2mQYKZqLFKARB9P0hoTe1ePuaX6cQaZFzDLzeD1E/gSJ16DEJnB8qhfDMYNF2op+mPbiDlm47WPDdl1NZ4xOFvi6+vJ88PNWjEZdf/75J23btiUiIoJatWqxbds2AgJst1KsJCNCiILp/n2496Sy54MHhq9vvgn/+59VXl5RQBub8mEeE2vH4fplTI5p2ECPfzE763cy3HsdIn54KqCErJ9fcju421a5dWta1XMVHSp2sOgcR3tHHMwtwJcP7dmzh06dOhETE8OLL77I5s2bbW6MyNMKxndeCCFSu3QJqlSBpKfGYvj6QvmcL7aWWdGylPLtWUhEtIcg6aklau3cwCODFXDTGxOhKGl7O/yfFBTTOINbQ3CurcotmDuP79BvTT9Co3Ovh+pqhOHWmLO9M66OGVSNLcBu3bpF+/btiYuLo2XLlqxduxYPjyyU5leZJCNCiILn4kVDImJvD/7+hjYvr2wtImeubHtMTPqJSMOGhktm+fM+fBpE/27a5lAaKmSQjJgNNAYefgF+qWa2aDRQdIxlr5NLdlzewd5re/PkWmWLZHB7qoArUaIEX375Jfv27WPFihW4uLioHVKWSDIihLAdly/D9u2GDCAnzpwxfK1ZE44dy/bLZKVs+9NFy3I8DiS7bnUCjy4qXDhrkme4vFDiBb5q+VWuXSfYK5gKvhVy7fVtVUJCAk5OTgCMHj2aUaNGYWeXf2qiSDIihLAdPXvCiRPWe71sLmuf3BuSUQ8IZNALoiRB2BcQsxF0j8CzW+6vv5J4FYq8mbvXyERkXCRf7P+CB9oHafZdDL8IQFHXojQt0zSvQyvQZs6cyZIlS9i1axfe3oaaMfkpEQFJRoQQtiQszPC1ZUsoUiRnr2VvD6+lrK6a2Sq5qY9r3BhOnjRtN1e2Pd1ekNB3IOK7lOdJaT+cc8S9helKuo4VDAmPyuu8/H7ud7458k2Gx/i6+uZRNAWfoih8+eWXjB9vuDW3dOlShg8frnJU2SPJiBAidyQmwvjxlq0om5yMTJkCdepYLZScrpJr0TgQRQdRS80HYa37N159DA8bE5cUB0C1gGoMqDEgzX4neyd6Ptszr8MqkBRF4f3332fqVEOP28SJE3nzTXV7xnJCkhEhRO44eNCQVGSHb87+en66FySz2y3m1KoFBw4Y8geLxoHoo0EfadoWfwoivociZv5q9Z8MRceatmmyd3spt0XFR7Hm7Bq0iea7mA7cOABApaKVeK/he3kZWqGi1+sZMWIEc+fOBWD69Om8++67KkeVM5KMCCFyR5zhr2SCg2GMBTM6nnkGymZ/NkRWp91mxiQB0cdB+EzQ7gMlNuWgkjtAY+bXqOuL4NYCHn5peB5/HHSdzV/I+ZnMg7ERkw9M5qtDmQ8+dba3zWSqIEhKSmLQoEEsXboUjUbDDz/8wKuvvqp2WDkmyYgQIncVKwajRmX5cEUBbQ6qtlt12m2yO70her2ZHWZm/di5QomtEDH3qXZPCy9qe8K0httoz/o/SxX/KmaPcbZ35n8vWqewnEgrNDSUPXv24ODgwC+//ELv3r3VDskqJBkRQtiMnI7teFqWpt3qYyHub8OtEdd6aV8k8SbEHjKs5ZJ0J/OLapzA3iltu1tLi2K3JbGJsZx5cMY4S6Zv9b582PhDlaMqnIKDg9m5cydXr1616bVmLCXJiBAid4SHZ3qINcZ2pCfTXpDwmfB4DcTuNzx3bQClD6U9ThcOJXfBg3FZS0aeZucF/l+DS03Lz7URjX9qzLG7KfVaNLm3sp8wIzIykhMnTtCsWTMAqlSpQpUq5num8itJRoQQ1vfbb/DKKxkeYq2xHenJdNDp/VFZe6HsJhHeQ8CjMzhVMD+uJB85//A8AEEeQfi7+9OxkoXVYUW2PXjwgDZt2vDff/+xadMmWrbMvz1sGcnf/0OEELbp2DEUQIsbdHoZzIwByZWxHVHLIXKpofiXJhtd2EqCoSaIY3DmxzpnkqQ4+BkeBciBwQco75vztX1E1ty+fZtWrVpx9uxZ/P39KVq0qNoh5RpJRoQQ2RcVBTt2GGqKpKKc+Y9GHOQwDWEihkcGclxSPfYoXH8y3sOxInhkMxG53Rt8/2c+GXFrYRg34toIvPuBxtHyawiRRVevXqVly5ZcvXqVEiVKsHPnTp55Jv/MvLKUJCNCFEJZrUaaqTfHwK9pC3zF4G5IRLIgw14QvRZiD4MuAtwag0OxdF4l1awW+yJwrX5Ku/cAKPJW2lM0rqbTdOP+BrsrELzC/CVsZME5UfCdPXuWkJAQ7ty5Q/ny5dm5cydlypRRO6xcJcmIEIWMdWes/PDkkb7Mxn6k6QWJ+wdidoJ2B8RsS2kvuR0cWmUeUtxfps99hmV+jvEa2wyzYYRQydWrV2nSpAlhYWE8++yz7Nixg6CgILXDynWSjAhRyGi11puxkhmLx35E/AT3hmK2fkd2eXQy317uUsp1NM4FbnxHVkTFRzF43WBuR93O8LiYhBwUfhEWKVWqFK1bt+bChQts3bq1QI8TSU2SESEKgdS3ZWJSfa5ka8bKuXNQ96l1Y1auAjM1Dywa+6Ho4cF7pJ+IKBB/3rKKpaX2g0Og+X2OxbP+OgXU3mt7WXN2TZaOdbZ3xs+t8CVsec3e3p5FixYRFxeHp2f+L5SXVZKMCFHAZXRbxt09G8mIEg1owcsL3nkHgoKgSwhkdndD9xgSzpFmfIdTxSevG2uoxwGgPBkQq8Qb6nsoWggdCUVGm09G7APAZ0TKc+eq4Nk9gzEmAkCn1wFQ2a8yU0IyXkeoil8VvF288yKsQmfdunWsW7eO+fPnY29vj6OjI46OhWuAtCQjQuS26Gjo18+y1WszoSig1btk6dgYvSuHz+xO097Q/QRuDYdgcf2q5C4WHx/47LPMj1cUCBsPD78CdKb7PHtA8CrDtp07+AxJ2aePhpvtDYkIQMIFcE9nzIhTGQicbcGbKJw2nN/Azis7jc8vPboEQFHXonR+Jp21c0SuWrp0KQMHDkSn0/Hiiy8ybJgFY5wKEElGhMhthw7BenPrmmSPAinTZi0USgDuT4p+uMVo0fyTg0BKl87acTFbDYlI4Pdw77WsnZOciMQeSGkLWgpO5SyPUwCGJedf/u1lsyvuejl7qRCRmDdvHm+++SaKojBw4EAGDx6sdkiqkWRECGvT6VJWrIWUnoQKFWDOHIteSlFAG29v0hYTZ8/h3pYnIg2ffYT/tF8sLyRmjkYDL7xg2NbHwqOZEH8Giv2f4dZLarEHofgyQ69GVpMRjTOU2PRk287QayJyLDkRGVV/FO5Ohu+pg50Dfar1UTOsQumbb77hvffeA2DEiBH83//9H3Z2dipHpR5JRoSwpocPoXp1uHs37T4vL2jdOssvlZUpuJYMQHVzK4JGk/XrZyjxOtwfCqGHIenJTIzg39ImIgBF3jXMVNFFZP31NY5gX7jumVviXNg5jt05lvmBqSipxup82PhD/N39rR2WyAJFUfj000/57MktznHjxvHll1+iscpfCfmXJCNCWNOZM+YTEchSIvL0rJeMEpFsl0zPKSUBbjSHxKspbYELDeuwKEmpDrQ3BFcIp8zmpgRdAvUX1CcqPirbr+FgJ7/61XLx4kW++uorACZNmsS4ceNUjsg2yE+kELmhUiU4cSLluUYDrq4ZnpJRT4i5HhCLS6ZbS+yfpokIwL0hhkdqlaJBk063TcD/PVm7pWTuxFiAxSbGGhORlmVbYqexrGu/canGFHE104Ml8kSlSpVYtWoVN2/eZMSIEZmfUEhIMiJEbrCzM2QLFkivGJlqPSDp0TiD9yDTtshFps/dQtKO87DzhAr3ZLptFkTGRXI/5r7ZfY8THhu3N/fbjJO9VIy1dYmJidy+fdtY0r1zZ5m59DRJRoSwQal7QlTrAUmPaz3DI9mjOWmTkZKb056nsZdEJAtuRd2i0qxKxCbFZn6wsHmxsbH07NmTEydOcPDgQcqWLat2SDZJkhEhbFC2ipHlNn0coDPt8Yg/B7FHwKsfaFzArcmTbft0X0Zk7MLDC8QmxaJBk+GU2w6VOkiviI17/PgxXbp0Yc+ePbi4uHD58mVJRtIhyYgQNkKx4nIsVpV4B242NxQdK7EBPDqm7HOuDMV/US+2AuzZgGc59eYptcMQ2RQeHk779u35888/8fT0ZOPGjTRp0kTtsGyWJCNC2ABFgcaN1Y7iKYoCN9sYVs81Krx1ELJDp9fRakkrTtw7kfnBTyTqEnMxIpEXQkNDad26Nf/++y++vr5s3bqV559/Xu2wbJokI0KoKHkqb0wMnDxpaKtVy+Kxr7njVoenEpFUdOFg75u38eRDVyOusufanmydWyuwlnWDEXni1q1btGjRgosXLxIYGMiOHTuoVq2a2mHZPElGhFBJelN5DxywgQGrSXchZgt4dDFM5dXde9IeCnEn4cHHUHKjqiHmJ+6O7hx7LetFyuw0dpT3LZ+LEYnc4uHhgYeHB6VKlWLXrl1UqFBB7ZDyBUlGhMgjqQuagfmiZg0b2sjA1fhTEPQzeA+Aa3VTkpF7Q8DOC7z6qxufilb/t5oFxxeYVDRNT3L5dXs7e57xM7PasChwfHx82LZtG3FxcZQsKXV0skqSESGs4fvv4b33ID7e7O7MSrsnT+XNs2m8iddBewiUJ/G6NTesfJvMLcSwJow5zjXB/8tcD9FWfbLnE86GnbXonECPwFyKRtiCo0ePcvToUYYPHw6Av7+U2reUJCNCWMPatYaujmT16wOmY0LSS0TyvKhZ+Ey4PyrluddAQw9IaqkTkcRbKdul/wDX+rkanq1L1BsGmH7c+GMqFa2UpXMal7a10cnCWvbt20fHjh2Jjo4mMDCQbt26qR1SviTJiBDpiY+HU6eyNuc2MtLwdeZM6NEDgoLS7Q15urR7nhY1S3oA99819G7E/2Noc6kNEd+nHOPe0bSXxOV5wwJ4xWYUyEGrYdowfjz+IzGJMZkfDDzUPgSgbYW2NCxl+erJouDYsmUL3bp1Iy4ujubNm9OqVSu1Q8q3JBkRIj0dO8LOnVk+XAG07gHgXRzS6Q1RvbR73F9QbDZoHODe64a21L0kAKVqmCYjJTfkVXSqmPnHTL448IXF57k72cLgHqGWVatW0a9fPxITE+nYsSOrVq3CxcVF7bDyLUlGhEjP+fOGr8WKgbNzhocqCjR6tIHDr9aAV9Puz/MxIUkPIOE8uDY0vaBbCNg5Q8QP5s+z8wCXeub3FVDJi87VCarDiyVezNI5FXwrULNYzdwMS9iwRYsWMXToUPR6Pb1792bJkiU4OjqqHVa+JsmIKJyiomDFCtNxHuaOAdi0CerUyfDltDFw2MP8vjztDVF0cP1FiDsKxZelvahdBkmVfTEovhzsCtZfdzEJMey/vh+dojO7/2qEYQXithXa8kULy3tIROHyzz//MHjwYACGDh3KvHnzsLeX5Q9ySpIRUTjNmAETJmTtWAu7XlUbE6Lo4PyT/9LONcHlOUMJdwCcTG+9JHMoDoHzwKkSOFZIfwZNPvbqhldZfnp5psfZy3o6Igtq1qzJxIkTefToEdOnT0ejelGggkGSEVE4hYcbvlataih5mp7KlQ3HPMVczZBkqi1yF3skZTv+H7iSqq6Fc00oezLluc9rhkchcDPyJmC4teLran4ArpezFy9XezkvwxL5iKIoxMbG4vakNPL48eMBJBGxIklGRMH144+wZYv5fcm117t2hS8tq5mh1xvu2iS/hM2IPwN+n0HYJ2n3uTbK+3hUEp0QbSw2BpCgSwDg65CveanKS2qFJfIpvV7PyJEjOXnyJNu3b8fd3V2SkFwgyYgomBQF3nwTEjNZdMzC4kSKknEi0rChiuvK+AyF+LPmkxH/z/I+HhXsu7aP1r+0NiYgQuREUlISQ4cOZfHixWg0Gnbv3k2nTp3UDqtAkmREFFzJicjXX4OHmdGlXl6QxQJF5ha0q1gRjh9/arJKXowPUXSg3QtJd8A7VVl2TTr/nStGgL13LgdlG47eOWo2EQn2DOb5YFk1VWRdfHw8ffv2Zc2aNdjb27N48WJJRHKRJCOiYDlxAvr0gUePUtoGDwY/v2y/ZHrFy44fN5/j5CpFB1cqQ+IlcHrGNBkBwwBUO09QksCzKwTOB7vCVw+jf43+LH5psdphiHxKq9XSrVs3tm3bhpOTEytXrqRLly5qh1WgSTIiCpZNm1LqgwAEB4N39nsFFAUePLCBBe3iz8PDSRCV6gPWrkja45yfhUpReRdXLjl+9ziD1g4y1gDJqsj4yFyKSBQWkZGRdOrUiQMHDuDm5sa6desICQlRO6wCT5IRUXAoCty4Ydju3h0++QTKlYNsFiMy1yOS58XLwFDA7EazlJVznxZ/Buz9wSEgjwLKfevOrePU/VPZPv+ZorJCrsie+/fvc+7cOby9vdm8eTMNGjRQO6RCQZIRUXB8+CHMn2/YLloUatTI0ctptaaJiGql3GO2p5OIKBB3Eq7Vhor5vzckNQXDekA9qvbgvQbvWXSuq4Mr1QKq5UZYohCoWLEi27dvR1EUateurXY4hYYkI6LgOH48ZbtzZ6u+dGioimvKOFeHwJ9M2x5+AXF/GhIRAHvPvI8rG+b+PZfdV3dnetzp+6cBKOZejHrBhas8vch7165d4/r16zRt2hSAWhnVHhK5QpIRUTBcvgzbtxu2lyyBDh2s+vLu7nmQiMTshKhfIek2+H0Ork8+hF1qGB7Jwr6AxMspz0v/lcuBWUdsYiwjNo9Ar+izfI6fW/YHHguRFefPnyckJITw8HB27drFCy+8oHZIhZIkI6JgaNkyZTu/LVgVsxNuplp6POD/UhKRp2n3Q+whcG8LTpXBewi4VM+bOHNIp+iMici01tNwsnfK8Hh3R3d6VO2RF6GJQuqff/6hVatWPHjwgCpVqlCyZEm1Qyq0JBkRBUNoqOFr8+bQtq26sVgi7iTcStWL4/s/8OwMiddT2uyKgL2XYdutieFhgxRFYe25tVx5dMXs/nhdvHH7zbpv4uromlehCZHGkSNHaN++PRERETz33HNs27YNvxyUABA5I8mIyN8UBaZPh7g4w/OffsrRVN489/g3UFIV6QqfZnikFrzOkKDYuNP3T9NtZeZF5BzsHLC3k0XphHp2795N586diYmJoWHDhmzatAnv/PR7owCSZETkb2fPwpgxhm17e/DM3kDOpxe+A9PF73KN7iFoUhcsSTRNThxKg1vzPAgk58JjDYsPejl70eWZ9AtEtSjbItNbNELklr///pv27dsTHx9P69atWbNmDe6qrGwpUpNkRORvqTOIDRvA1/yqrMnMJR2KAo0bq7TwXeB3hgeAkgh3+hh6S8CQhAQutOmZMom6RP6+8zdJ+iT+Df0XMJRel+qnwlbVrFmTtm3bYmdnx7Jly3B2dlY7JIEkI6KgKFUK2rXL8JD0yrpnJs8Wv3v8O+AAfp+C1yvgVD4PLpozwzYM4+d/fjZps9PYqRSNEOlTFAWNRoOjoyMrVqzA3t4eBwf5CLQV8i8hCrTUPSExMRknIrVqwYEDaafwWq3aqj4WYv8A/WNwKA6udU33e/UyPPKRy48MU4yLexbH08kTO40db9V7S+WohDA1ffp0zp8/z9y5c9FoNNIbYoMkGREFVkY9Icll3VPL1RLvUcsNt2AAHEpBqb25dKG8oSgKCboE41TdWe1m0a1K1lZAFiKvKIrCZ599xqeffgpA586d6WDlGkTCOiQZEQVOcm9Iej0heV7WXVFSEhEA/y8Mhc2SbhueOwSCU4U8CibnkvRJ1JtfjxP3TqgdihDpUhSF9957j2nTDLPTvvjiC9q3b69yVCI9koyIAiW93pDUPSFW7wGJ3gqPZoPfR+D6Ytr9STfBsQIkXjI8vzvAdH+R0VBsWtrzbFRodKhJIlLEpQi1A2UND2E7dDodb775JvOfrFU1c+ZM3n77bZWjEhmRZETkP2FhcOSIYfviRZNdTy9uB7nYExL/H1x9NuW57zvmj7P3h6Cf4UZD8/vdmlo5sLxhr7En/P1wXB1ccbTPZ1VvRYGVmJjIoEGD+PXXX7Gzs2P+/PkMGTJE7bBEJiQZEflPSAj8849pm33aIlrJvSG5NhYkdSICwJOLKIrpBe1cQZNOka8i74BHp1wILvdpNBq8nL3UDkMIE8eOHWPlypU4ODiwdOlSevXKX4PCCytJRkT+c/Om4WuNGuDqavjgHzo0zWHu7mkHqVqNojPfrouCh5MhYHLG5xf9BHzfBvui1o9NiELshRde4JdffsHT01PGiOQjqicjc+bMYerUqdy7d4+aNWsya9Ys6tVLf8nwGTNm8P3333Pjxg38/Pzo0aMHkydPxsXFJQ+jFrnm7l344Ye0lclSSy6Nunw5VKmSN3GloQfn5yD+eEpTzFbDgnde/dMe7vIcVAwDHMBeyk4LYU0RERFERERQpkwZAHr37q1uQMJiqiYjK1asYPTo0cydO5f69eszY8YM2rRpw/nz5wkICEhz/K+//soHH3zAwoULadCgARcuXGDQoEFoNBqmT5+uwjsQ1mBSFXXSTJg9K5Mz7AE3sPOEp0q250kJdwCNIzhXNk1GkteU8R1j/njpBRHC6u7fv0/r1q2JioriwIEDBAcHqx2SyAZVk5Hp06czbNgwBg8eDMDcuXPZtGkTCxcu5IMPPkhz/OHDh2nYsCF9+/YFoEyZMvTp04c///wzT+MW1pN29stXTx5ZUDmXgkot8TYkXjYMQnXOQi9MuSvgVDb34xJCcOvWLUJCQjh//jzFihXj0aNHkozkU6rVbU5ISODYsWOEhISkBGNnR0hICEeSZ0o8pUGDBhw7doy//voLgCtXrrB58+YM7wvGx8cTFRVl8hC2w9zsF2vIcQl3fTzc6gaXSxhqhJgbgOrzhmGWTNASKHsGntFLIiJEHrl8+TKNGjXi/PnzlCxZkv3791OtWjW1wxLZpFrPSFhYGDqdjmLFipm0FytWjHPnzpk9p2/fvoSFhdGoUSMURSEpKYk33niDDz/8MN3rTJ48mYkTJ1o1dpF9Ty9Ul/q2SmgouI8dAT8vgk8mwNix2b5OjmfQPPo/iP7dULa91B5wqmTmIo2Bxjm4SP6SqEtkw4UNXA6/rHYoopA7c+YMrVq14u7du1SoUIFdu3ZRqlQptcMSOaD6AFZL7N27l0mTJvHdd99Rv359Ll26xDvvvMPnn3/O+PHjzZ4zbtw4Ro8ebXweFRVFyZIl8ypkkUpmC9W5u4O7YwKgBadEUHNVb+0+w1ddONzuYdhOvAWOJcD7VcNMmEJmxZkV9P89ZXCuk72TitGIwuqff/6hRYsWhIeHU61aNXbs2EFgYKDaYYkcUi0Z8fPzw97entDQUJP20NDQdH+wxo8fT//+/Xn11VcBqF69OjExMbz22mt89NFH2Nmlvevk7OwsiyLZiIxuyTT0+hc3j5p5G1BGlNgnX+Mg/lRKe/wjcCyct2Lux9wHINgzmDrF69C5UmeVIxKFUXBwMIGBgZQvX56tW7fi6+urdkjCClRLRpycnKhTpw67du2ia9euAOj1enbt2sVbb5lf9VOr1aZJOOyfFLtSFCVX4xXWZbJQ3YMHuJWtifGuiqMjZDC9W1Xew8CjcCy0FRkXyfXI68bndx7fAaB52eYseWmJWmGJQs7Pz4+dO3fi4eGBp6en2uEIK1H1Ns3o0aMZOHAgdevWpV69esyYMYOYmBjj7JoBAwYQHBzM5MmGAlKdOnVi+vTp1K5d23ibZvz48XTq1MmYlAgblJgIx4/DYz1gWLvF/d8juLsaVnzl0aOUY0NDDQM+PDzyPs7M2PlA0A9qR5EnYhJiKPd/5QiPDU+zT0NerTAohMHvv/9OWFgYw4YNAyAoKEjliIS1qZqM9O7dmwcPHvDJJ59w7949atWqxdatW42DWm/cuGHSE/Lxxx+j0Wj4+OOPuX37Nv7+/nTq1Ikvv/xSrbcgsmLYMPj5Z8ANY2GQViHAU4XNNBowU18m18WdgKjl4P9VyqjXUrvyPg4bEhoTakxEAj1Sbpu6OLjQ61kpry3yzpIlSxg8eDB6vZ5KlSrRtGn+XMtJZEyjFLL7G1FRUXh7exMZGYmXl6yrkZuMM2dCQuCPI8T4l6XYg9MARJevibtdrOkJXbrA1Kl5G+SDT+Hhk9lWz+hzaRGb/OfKoyuU/7/yuDu6E/1htNrhiELqu+++Y8SIEQAMHjyY+fPnSy+4ynLrMzRfzaYRNmztWjh/3vhUUaDR3H4cvl4C2GlofJDq+H/+UXe2jD4WrtaAxEsqBmFblp9ezhsb3yAuKQ69olc7HFHITZkyxVj88u233+bbb781O0lBFAySjIgcUy5dRvtSP5O2GNw5zPtmj89xQTJruJBOAEoSRC0F74F5G48N2HBhA5HxkSZt9YJtdCCxKLAUReHjjz9m0qRJAHz00Ud8/vnnaKTXskCTZETkiKJAo+7FOPz0IjGphPZ+G/fSfvD+++DsnPOCZDkVf958uz4SLhYBn7cKZTKS7JMmn/Dqc4bp88FeUlpb5K1t27YZE5EpU6YwNgfFD0X+IcmIyBGtFg7/m/7Ml4YNwX/Z/9nWUAztHvPtF4sYvvoMzrtYbFAR1yKU9JbCgEIdbdq0YezYsZQuXZrhw4erHY7II5KMiBxJPfw5NPg53M8fN9mvei+IOT6vg89rKc9jtsGtJ+sbBf4ALs+pE5cQhVRCQgKJiYm4u7uj0WiYMmWK2iGJPCbJiMg2RYHGqZZmcddoUwqZ2TKNBpJrZegepiQi5S6CUwXVwhKiMNJqtfTo0YOEhAQ2btyIi4uL2iEJFUgyIiySeqG7mBg4edKwXYsTuGli0z1PVYl3QLsXXOulTTYS70CZf8G5mg124QhRsEVFRdGpUyf279+Pq6srp06d4vnnn1c7LKECmSclsix5oTsPD8Mj9YLLB2hsm5/lYV/A5WBDLRFza8q4VDc8bDJ4IQquhw8fEhISwv79+/Hy8mL79u2SiBRi0jMiMpXcGxITY36hu4bPPMD9fAxQNM9jy9DjtRD2ZDVnlxfgXqpxIkWGg0sdVcISorC7d+8erVq14vTp0xQtWpRt27ZRp478fyzMJBkRGUruDXk6CQkNBfdvv4CvJuN2Xmubq5WEvp2yHbXYdJ/Xy3kbixACgOvXrxMSEsKlS5cICgpi586dVK1aVe2whMokGREZ0mrTJiING4K/P2jOHQO0huV3XV1h0CA1Qkxf0u3097k2yLs4hBBGERERhIWFUaZMGXbt2kW5cuXUDknYAElGRJaFhhryDjc30Fy+ZCgBDzB9Orz2WobnqsMeMFPWvPhysMsP036EKHhq1qzJ9u3bCQoKokSJEmqHI2yEJCMiQ6nriLi7kzJ1t1OnlB1OTnkaU5aV+QNQQEmA2z0g6Q4UXwlePdWOzKYk6ZO4HXWb6ARZEE/kjj///BOdTkeDBoYeSRmoKp4myYhI19N1REzcv2/42qgRdO6cZzFZJHXxsgoZ3LIp5BotbMSft/9UOwxRQO3du5dOnTphZ2fHoUOHqFatmtohCRskU3tFurTaVHVEaqVa3O6bbyA83LD9ww/g66tCdMJajt45CoCLgwslvErQomwLlSMSBcWmTZto164d0dHRPP/885QpU0btkISNkp4RkSUHDjwpxXHlCrz3nqFRowEfHzXDElZ09Z2rBHoEqh2GKCBWrlxJv379SEpKonPnzqxYsUKqq4p0Sc+IyBJjTbC4uJTGTZsgKEiVeDIU/x9EzAclUe1IhCiUFi5cSJ8+fUhKSqJv376sXr1aEhGRIUlGRPb4+UG7dmpHkdbj9XCjyZPy7o5qRyNEobNx40aGDh2KXq/ntddeY/HixTg6yv9FkTG5TSMKDkWB210M24/XGWbRAGAHbumNxBVCWFOrVq1o1aoVNWrUYOrUqWhkqQWRBZKMiDRSl383Cg0FNwUePFAtrkzd7Z+yHT7F8EhWWUl7fCGnKAof7/4YvWKmFosQFlCe1ADQaDQ4OzuzceNGHB0dJRERWSa3aYSJ1IvhpV4Ij/LlDONDmjVTK7SM6bUQtdSw7T1M3VjyiSuPrjDp4CTAMJPG08lT5YhEfqTX63nzzTf53//+Z0xKnJycJBERFpFkRJgwW/7d6ShuaA1PNBrDo3v3vA8uI7GHQeMKleJA42y6L3CeOjHZuARdgnH78JDDuDtJVVphmaSkJAYMGMC8efOYMWMGx48fVzskkU/JbRqRrtANf+EeeQe3t19FEw78/TfY6sqajuWh4iOwc067z7NH3seTj/i6+lI7qLbaYYh8Jj4+npdffpm1a9fi4ODAkiVLZOVdkW2SjAgjRTEdJ+LeqTnuyT0iAA42/OPiVDbVE13KZpnjYC9F2YSwppiYGF566SV27NiBs7Mzq1atolPqJSKEsJANf7qIvJQ8VuTpWzT4+ED16lC5suFrfpBwyfA16Gdwkb/4hbCmyMhIOnTowKFDh3B3d2f9+vW0aCFVe0XOSDIigLRjRRr6nMYtQgv1G8PWreoFlh2Bs8E+AOx91I5EiALn0KFDHD58GB8fH7Zs2cILL7ygdkiiAJBkRKQRSgD+EQ/QAHh5qR1O+hQ9RC2D2D/A7xNw8De0O1VSNy4hCrD27dvz888/U6NGDWrWrKl2OKKAkGREpOFODJp+/aB8eRgwQO1w0nfvdYhaDMFrUhIRIYTVXb16FUdHR0qUKAFA//79MzlDCMtIMlLImS1wBvD669DYhquWJoVC5ALDdvxZ03Vo3JqDvbc6cQlRwJw9e5aQkBC8vLzYt28fAQEBaockCiBJRgqxdAetAjz5C8hmhU9P2X7wnum+CqF5G4sQBdTx48dp06YNYWFhFClSBJ1Ol/lJQmSDFD0rxMwWOOMgbqePQtmy5k+yFbEH09/nIH+5CZFThw4donnz5oSFhVG3bl327dtHkC2u0i0KBOkZEYBh0Ko7MbihRfNsPljHxT6dhKPsqbyNQ4gCaMeOHXTt2hWtVkvjxo3ZuHEjXrY8mF3ke5KMFFJpCpwRYyhw1ry5ekFZws4N7J6MC9FHprQ7V1Mnnnxg9l+zmfXXLOP6IfG6eJUjErZo27ZtdO7cmYSEBNq0acOaNWtwc3NTOyxRwEkyUgiZHSsy8m34/APbnsqbWvGlKdtRv4HGETw6qhdPPjDn6BwuPLyQpr2CbwUVohG2qnr16pQoUYLatWuzdOlSnJ3NLLEghJVJMlIIxcQ8VeCMg7h52IG3Dc9A0T2G2H2GGiJP1xHxsrFF+2xUco/InPZzqFkspT5ErcBaKkUkbFHx4sU5ePAg/v7+ONjyEhCiQJEBrIWMopjO2A0d9jEHaIzNrvatPQy3e8FFL7jVCWKPqB1RvvTzyZ85//A8ANUDqtOwVEPjQ1brFdOmTWPp0pTexqCgIElERJ6Sn7ZCRquFkycN27UC7+H/7y5sNQ8hYj7ce03tKPI9nV7HsA3DjM+LuhVVMRphSxRFYcKECXz++efY29tTu3ZtqlatqnZYohCSZKQQO3CvApp7T0ax2toANUWBsAmgcQIlIe3+uBOyCF4WKSgk6g1F4RZ1WURVf/mwEYZEZPTo0cyYMQOAzz//XBIRoRpJRgqRp2fQaFBgyBAoWhRefVW9wMzRhYHuIQT/BuHfgna3oT1mJyTeBt19SUayaPvl7cbtzs90VjESYSt0Oh2vv/46P/74IwCzZ89mxIgRKkclCjNJRgqJdKutzpple70iABp7QyLi0REezUlpj/oF0EDJ3aqFlp/ciLxBh187AKBBg6O9o8oRCbUlJCTQv39/Vq5ciZ2dHQsXLmTgwIFqhyUKOUlGComnq6025CBuPs7g5KReUBmx902Zqqt/bLov6Bdwb5bnIeVH4bHhxu3Z7Wfj4eShYjTCFixdupSVK1fi6OjIr7/+So8ePdQOSQhJRgqjUALw5wGaI2chP4yYt/M0fHWpD6V2GwqeCYsU9yzO8OeHqx2GsAGDBg3i33//pXXr1rRr107tcIQAJBkp+PR66NIFdv8J3AcM1VY1zz0HlSurG1tWubcGj07g9YokIkJkw6NHj3B1dcXFxQWNRsO3336rdkhCmJBkpKDbsgU2bgSe+hCvW1eVcDKkJELsUcMMGtdU8fm+q15MQuRzoaGhtG7dmtKlS/Pbb7/h6CjjhoTtkWSkoJs+3fD1tdfhhydtZ8/BMyVUC8msxDtw88m6OKX2qhpKQaEoirHqqiicbty4QatWrbhw4QL379/n1q1blLX1FblFoSTJSEF24QLs3g329vDuuynJSMmS2Fylszu9IeEC4GgodpbM9QXDbRphke2Xt9N9ZXeiE6LVDkWo5OLFi4SEhHDjxg1Kly7Nzp07JRERNkuSkYLs3j3D1woVDAmILYs9+GQj0VDsLFnRjyUZyYbdV3ebJCJNSjdRMRqR106dOkWrVq0IDQ2lUqVK7Ny5k5K2/jtAFGqyNk1hoNFg8731rg3Nt7s1y9MwCpo36rxB2Hth/NrtV7VDEXnk6NGjNGvWjNDQUGrWrMn+/fslERE2T5KRQuDpxfFsUsAM8+1uLfI0jILG1dGVom5F0djsSojC2nQ6HfHx8dSvX589e/ZQrFgxtUMSIlNym6aAU4AHOt+UxfFq2WbBVTRm8uJSh7Dd5YSFsE0vvPACu3fvpkqVKnh6eqodjhBZIslIQfTXXzB7Nsqt2zTiIIcvpdwCOXDARj/fNU7gWMaw7VQFirwNbg1UDUmI/GLNmjWUKVOG5557DoB69eqpHJEQlpFkpCD68kuU9et5gD+HSUlEGjYEd3cV48qIczUof1XtKAqEsw/OMuXQFLXDEHnk559/ZsiQIRQpUoS///6bMmXKqB2SEBaTMSMFkBIbRyMOUuxJxVWA0FAb7hURVjVx30Tjtrezt4qRiNw2e/ZsBg0ahF6vp2vXrjJQVeRbkowUNGPGoN1xME2PiL+/JCKFRUxiDABV/Kowsv5IlaMRuWXy5MmMHGn49x01ahTz58/H3t5e5aiEyB65TVPQfPcdqSuahYbaWCKScA2upCq85Phku+g48BmmSkgFweGbh7n48CIANyNvAjCmwRh8XX3VDEvkAkVR+PDDD/nqq68AmDBhAhMmTJAZUyJfy1EyEhcXh4uLi7ViEdagKKRORtzdbSgRURLhVnvTtsQn40R0kXkfTwFx9dFVGi5MW6fF0U7WICmI5s6da0xEvvnmG/73v/+pHJEQOWfxbRq9Xs/nn39OcHAwHh4eXLlyBYDx48fz448/Wj1AYTnF5mq9PxH7JyScVTuKAud+jGFskIuDC+0qtKNdhXYMqjWIDpU6qByZyA0DBgygSZMmzJs3TxIRUWBY3DPyxRdf8PPPP/P1118zbFhKt3q1atWYMWMGQ4cOtWqAwjKKAo05oHYY5sWfTn+fQ2DexVFABXkEsbnfZrXDELkgMTERBwcHNBoN7u7u7N69W8aHiALF4p6RxYsX88MPP9CvXz+T/ww1a9bk3LlzVg1OWOjoUbTxdpykNmCDBc7cmkHgjykPr4FPdtiBeys1IxPCZmm1Wjp16sSnn35qbJNERBQ0Ficjt2/fpkKFCmna9Xo9iYmJVglKZMPNm1C/vkmTzU3lda4MPkMMD5eaoN0L7u2gzF/gICWrhXhaZGQkbdq0Ydu2bXzzzTdcv35d7ZCEyBUW36apWrUqBw4coHTp0ibtq1evpnbt2lYLTFgoNJSnV8OzqUTkaS51oMI1taMQwmY9fPiQNm3acOzYMby9vdm8eXOa37tCFBQWJyOffPIJAwcO5Pbt2+j1etasWcP58+dZvHgxGzduzI0YRUbOn4e5c+HWLcPzEiXhlrohmdBFQcJ5sHMH56pqRyNEvnD37l1atWrFmTNn8PPzY/v27fLHnijQLL5N06VLFzZs2MDOnTtxd3fnk08+4ezZs2zYsIFWreS+f5777DOYMQNWr0YBYjxtZCCoosCdQXDRG240BV242hEJkS9cu3aNxo0bc+bMGYKDg9m/f78kIqLAy1adkcaNG7Njxw5rxyKyQ6sFQOnQkUb/zuHw2VIqB/RE6NsQ9bNhu+R2cGukbjxC5BOHDh3i8uXLlC1bll27dlG2bNnMTxIin7M4GSlXrhxHjx6laNGiJu0RERE899xzxrojIhfs2QMffABxcSltVw1Fw7RtXuLwppREpGFDlWfSRMxO2Y782fBIumeYwuvaAHwGqxdbAbD76m6mHp5Koi6RyHgpGFeQ9OvXD51OR8uWLQkODlY7HCHyhMXJyLVr19DpdGna4+PjuX37tlWCEulYuBD++sv8vlQD21QvAZ/41Ij/yAWmz11q5VkoBdXUw1PZemmrSVuQZ5BK0YicOnr0KKVLlyYgIAAwFDYTojDJcjKyfv164/a2bdvw9k5ZDVSn07Fr1y5Zujq3RUUZvr7xBnTvntLu7w8Vahqfql4C/vH69Pe5twbvIXkXSwGVpE8CYHjd4TQq1QiNRkPzMs1Vjkpkx65du+jSpQsVKlRgz549FClSRO2QhMhzWU5GunbtCoBGo2HgwIEm+xwdHSlTpgzTpk2zanAilSFDIDkhfOYZCAkx2a1EqxBTetybQ/EVT57o4U4fw6b3IEOxM40sFm0tDUo2oE/1PmqHIbJpw4YN9OzZk/j4eIoVK4aTk5PaIQmhiiwnI3q9HoCyZcty9OhR/Pz8ci0oYcbOnSnbDRqY7FIUaNw4j+PJiHM1wwNArwW/TyHiB/D/WhIRIZ5Yvnw5/fv3JykpiZdeeolly5bh7OysdlhCqMLiMSNXnwyYFLksKQkOHzbOliE21vD1jz+gXj3jYYoCDx7AyZOG5zZXAj72T0i8BaX2gIO/2tEIYRMWLFjAa6+9hqIo9O/fn4ULF+LgkKNF1IXI17L10x8TE8O+ffu4ceMGCQkJJvvefvttqwRW6H31FYwfn7Y91V9OigKNGhlylmQ2VwLevbnhIbLscvhlTt0/leExySv1ivxn4cKFxkVG33jjDebMmYOdnfQYisLN4mTkxIkTtG/fHq1WS0xMDL6+voSFheHm5kZAQIAkI9aSvAZFUJDhAVC5MlSvbjxEqzVNRBo2NAxeVZVeC3HHDdN35ZaMxbSJWmrOrUlMYkyWjre3kwXT8psWLVpQokQJ+vbty1dffYXGpv56EEIdFicj7777Lp06dWLu3Ll4e3vzxx9/4OjoyCuvvMI777yTGzEWbiNGwEcfZXqY6tN5AWJ2w82WUHS8FDnLpsi4SGMi0qBkgwyPDfIIonX51nkRlrCiMmXKcOLECYoWLSqJiBBPWJyMnDx5knnz5mFnZ4e9vT3x8fGUK1eOr7/+moEDB9KtW7fciFOYkXpdPNWn8+q1hkRE4wJ+n6gYSP6mTTSMEbLX2HNoyCGVoxHWoNPpGDlyJK1btzbOSpQJAEKYsjgZcXR0NN7fDAgI4MaNG1SpUgVvb29u3rxp9QCFeTY3gybiSWEzt2ZwuyeGZY/0gA5KZFB3RBjdjrpNhVkV1A5DWFFiYiIDBw5k2bJlLFq0iCtXrhAYaCPrRwlhQyxORmrXrs3Ro0epWLEiTZs25ZNPPiEsLIwlS5ZQrVq13IhRPKEoKZNrYmJsbAZN4mXD1xjTqqBoXPI+lnzqvwf/Gbd7PttTxUiENcTFxdGrVy82bNiAg4MDP//8syQiQqTD4hGGkyZNIujJgMovv/ySIkWK8Oabb/LgwQPmzZtn9QCFQfLMGQ8Pw6NYsZR9tjGDxgHc26VtLp1O+XqRrprFarKs+zK1wxA5EB0dTYcOHdiwYQMuLi6sW7eOnj0lwRQiPRYnI3Xr1qV5c8NUzYCAALZu3UpUVBTHjh2jVq1aFgcwZ84cypQpg4uLC/Xr1+ev9NZeeSIiIoIRI0YQFBSEs7MzlSpVYvPmzRZfN795euZMMpuYQQPg0S5V1dVUnKW3TBQujx49olWrVuzevRsPDw+2bNlC+/bt1Q5LCJtmtbmXx48fp2PHjhads2LFCkaPHs2ECRM4fvw4NWvWpE2bNty/b76GQkJCAq1ateLatWusXr2a8+fPM3/+/Py5sqWiQPv24Oho/rFgQZrDk4WGQnS04ZGnvSLaffBoLujN1J53DwHNU6WsnWvbQpeNEHlq3rx5/PHHHxQpUoSdO3fSrFkztUMSwuZZNGZk27Zt7NixAycnJ1599VXKlSvHuXPn+OCDD9iwYQNt2rSx6OLTp09n2LBhDB5sWE5+7ty5bNq0iYULF/LBBx+kOX7hwoWEh4dz+PBhHB0dAfLv4nxaLWzZkvExDg5Qt26awaru7ir0hsRsh7AvoNTOtEmHWY7g/1muhyWErRk7diz37t1jyJAh1KhRQ+1whMgXspyM/PjjjwwbNgxfX18ePXrEggULmD59OiNHjqR3796cPn2aKlWqZPnCCQkJHDt2jHHjxhnb7OzsCAkJ4ciRI2bPWb9+PS+++CIjRoxg3bp1+Pv707dvX95//33s7c0Xf4qPjyc+Pt74PCp55VtbcukSuLqmbXd3B29vtLYwWPXRHIg9AOedofgq8OyettdDYw++Y8HeDzw6gHNVFQLNX3Zf3c3cv+eiU3SERoeqHY7Ipps3bxIYGGicbThjxgy1QxIiX8lyMjJz5kymTJnCe++9x2+//UbPnj357rvvOHXqFCVKlLD4wmFhYeh0OoqlHokJFCtWjHPnzpk958qVK+zevZt+/fqxefNmLl26xPDhw0lMTGTChAlmz5k8eTITJ060OL48FRSU5QxDlcGqccchOtX03Ds9wbkGOJQyTNtNDkjjAAFT8ji4/G3C3gkcvHHQpM3fXdbwyU/OnDlDq1ataNKkCUuXLk33DyMhRPqynIxcvnzZOBq8W7duODg4MHXq1GwlItml1+sJCAjghx9+wN7enjp16nD79m2mTp2abjIybtw4Ro8ebXweFRVFyZIl8yrkHFMUwzTeZKoMwbj9suGraxOI3W/Yjv/X8BA5kqAzrO30Zt03qVGsBnYaO9pXlMGO+cWxY8do06YNDx8+5L///iMyMhJfX1+1wxIi38lyMhIbG4vbk7/eNRoNzs7Oxim+2eHn54e9vT2hoaZd06GhoenOxQ8KCsLR0dHkL48qVapw7949EhIScHJKO5bB2dnZtpbljo6G5csNS+1mwtxCeHku6R44V4YSa+HxmpRkBMCtpQxQzYFjd47x123D7LF2FdrR6ZlOKkckLHHgwAE6duxIVFQU9erVY8uWLZKICJFNFg1gXbBgAR4eHgAkJSWxaNGiNGWNs7pQnpOTE3Xq1GHXrl3GEsl6vZ5du3bx1ltvmT2nYcOG/Prrr+j1emMV2AsXLhAUFGQ2EbFJc+fCe++lPHdwgHS6dWNi0i6El+fjRey8DGNE7JwNyUhq7iF5HEzB0mV5F+O2q6OZMUPCZm3bto2XXnqJ2NhYmjZtyoYNG/D09FQ7LCHyrSwnI6VKlWL+/PnG54GBgSxZssTkGI1GY9GqvaNHj2bgwIHUrVuXevXqMWPGDGJiYoyzawYMGEBwcDCTJ08G4M0332T27Nm88847jBw5kosXLzJp0qT8tVJweLjha+XKULcutGgBZnpunp5Bo9pCeHaps59U84udnoEi+ej7boMexT0CoH+N/jQt3VTlaERW/f777/Tu3ZvExETatWvHb7/9hqu5AehCiCzLcjJy7do1q1+8d+/ePHjwgE8++YR79+5Rq1Yttm7dahzUeuPGDWMPCEDJkiXZtm0b7777LjVq1CA4OJh33nmH999/3+qx5Yp9++BJYkXbtvDtt+keqtWazqBRfUVeAH2k4av/ZPB93wYCyl8UReGf0H94FGtIQnR6HQATm03E0d5RzdCEBXx8fLCzs6Nnz5788ssv+adXVggbplGU1OW0Cr6oqCi8vb2JjIzEy8srby/esiXs3m3Y/vJL+PDDdA+NiTGUfQd4/DhlW1X6aNCovTxw/rXi9Ape/u3lNO3X3rlGaZ/SKkQksuvEiRPUqFFDZs6IQie3PkMtXihP5EByvZOXXoJ33snyaXn+2R8xHyKXglN5CJxnmLILYGcLGVH+dTXiKgA+Lj4EexqqBtctXpdS3qXUDEtkwcyZM2nVqhVVqxpq59SuXVvliIQoWCQZUcMrr9jIgjJm3H0VIn80bJdYn5KIiByLS4oDoFvlbvzY5UeVoxFZoSgKH3/8sXGB0NOnT8uMGSFygXzSiBT6mJRExCEYrr/wZIcCdp5QRlbgza4fj//IxH02XnxPmNDr9YwaNYpZs2YBMGrUKElEhMglkozklYQEuHhR7Sgypj2Qsp10G7id8tzl+TwPpyDZc22Pcbt52eYqRiKyQqfT8eqrr7Jo0SIAvvvuO9588011gxKiAMvWqr2XL1/m448/pk+fPsYVdrds2cKZM2esGlyBoShQrx6ksxqxzdA9TH9fEfO1X4RlJreczCs1XlE7DJGBhIQE+vTpw6JFi7C3t2fx4sWSiAiRyyzuGdm3bx/t2rWjYcOG7N+/ny+//JKAgAD++ecffvzxR1avXp0bceZ///xj+OrvDw0aqBtLujSk/EjoMNYVKTISvAeoFFP+Ex4bzsurX+b245SepVtRtwBwtrehasDCrIkTJ7Jq1SocHR1ZsWIFL730ktohCVHgWZyMfPDBB3zxxReMHj3apOJgixYtmD17tlWDK5D++w+eqlprM7z7Gh4A8f9B3FHw6Az2RdSNK5/Zd20fO67sMLuvbJGyeRyNsNTYsWM5ePAgH374IW3atFE7HCEKBYuTkVOnTvHrr7+maQ8ICCAsLMwqQQkb4FzV8BAW0yt6AKoHVGdm25nG9qJuRakeUF2tsEQGYmNjjVVUvb292bt3LxqppyNEnrF4zIiPjw93795N037ixAmCg4OtEpTIQ4k34eE0iNmudiQFjo+LD83LNjc+ahSrIR9wNujevXvUq1ePqVOnGtvk30mIvGVxMvLyyy/z/vvvc+/ePTQaDXq9nkOHDjFmzBgGDJBxBflKwgW4XAoejIG4k2pHI0Seu379Oo0bN+b06dN8++23REREqB2SEIWSxcnIpEmTqFy5MiVLliQ6OpqqVavSpEkTGjRowMcff5wbMYrcEHsUrjyTqkH+EhSFy4ULF2jcuDGXLl2iTJkyHDhwAB8fH7XDEqJQsnjMiJOTE/Pnz2f8+PGcPn2a6OhoateuTcWKFXMjPpEbkkLhekPz+6JWgFfvvI1HiDz277//0rp1a0JDQ6lcuTI7duygRIkSaoclRKFlcTJy8OBBGjVqRKlSpShVStbUyJdidgCJ4NoY9I8h/iSgwO3e8HilJCOiQPvzzz9p164djx49olatWmzbto2AgAC1wxKiULP4Nk2LFi0oW7YsH374If/9919uxFToKYph1d5ckxQKRcdBqX0Yb888eN+QiBQZnYsXFkJ9f//9N48ePeLFF19kz549kogIYQMs7hm5c+cOy5cvZ9myZXz11VfUqFGDfv360adPH+nmtAJFgUaN4PDhXLyIVy9wKPFkOWDFdF/AlFy8cMHzUPuQ65HXTdquPLqiUjQiK0aMGIGPjw9dunTBw0NWohbCFmgURVEyP8y8q1ev8uuvv7Js2TLOnTtHkyZN2L17tzXjs7qoqCi8vb2JjIzEy8srby6qKGD3pBPqwYMMi57FxEDq348NG8KBA0/yhuzSxxlmzjhVAjsX033nUr1wJS3YuebgQoVLeGw4Jb8tiTZRa3Z/k9JN2DdoXx5HJczZvHkzL774IkWKSAE/IXIitz5Ds7U2TbKyZcvywQcf8NVXX1G9enX27ZNfvNYUGmqFROTh13DRF67VhMTrafc71wb7QCjzjyQiFroZeRNtohYNGkp4lTB5lPEpw+Bag9UOUQALFy6kU6dOtGvXjphcvf8phMiubK/ae+jQIZYuXcrq1auJi4ujS5cuTJ482ZqxFXru7jlMRLQHDGNBMhK8AhzLgcY+Bxcq3Ip5FOPmuzfVDkOYMXPmTEaNGgVAzZo1cXFxyfgEIYQqLE5Gxo0bx/Lly7lz5w6tWrVi5syZdOnSBTc3t9yIT+REzK60bUlhYO8LmiedYk4yJdtSu6/upvvK7kTFR6kdikiHoih8+eWXjB8/HoD//e9/TJ06VSqrCmGjLE5G9u/fz3vvvUevXr3ws9UF34SBEm36XBcGd3pCmRPqxFNA7Lyyk4i4COPzF0u8qF4wIg1FUXj//feN5d0nTpzI+PHjJRERwoZZnIwcOnQoN+IouBQF5s5V7/oOxSHpjmH7ZgtQEtSLpYB5tfarfNb8MwI9AtUORaQyYcIEYyIyffp03n33XZUjEkJkJkvJyPr162nXrh2Ojo6sX78+w2M7d+5slcAKjJMnYfhww7aDA+TlPWuX58F7EFx9slKsJCI5tuvKLiYfNIyN8nDyIMgzSOWIxNNeeeUVFixYwGeffcarr76qdjhCiCzIUjLStWtX7t27R0BAAF27dk33OI1Gg06ns1ZsBcPjxynbK1aYzttNRVFAq7VysTOv3pB4y7TNqZIMVs2B2UdnG7dLeEldHVuhKIrxNkylSpU4f/48np6eKkclhMiqLE3t1ev1xiqFer0+3YckIhmoXBm6dTO7K7nQmYcHFCuWy3H4jsnlCxRsOr3hZ/zlai/zzgvvqByNAIiJiaFz585s377d2CaJiBD5i8V1RhYvXkx8fHya9oSEBBYvXmyVoAqL5LLvDx6krbjasCFka4KSPh70qQau2rmCZw/weQNKbAGfYTmKWRiElA3BwS7bM+OFlURERNCmTRs2btxI//79pY6IEPmUxcnI4MGDiYyMTNP++PFjBg+WIk9ppFPgNr3ekNBQiI62sNiZooPYP+FSGbjgBoo+ZZ99UQheBYHfg0fbbL8NIWzNgwcPaNGiBYcOHcLHx4d169bh7u6udlhCiGyw+E+71PdmU7t16xbe3t5WCarAOHAAmjUzu0urNd8b4u9vYaGzxBtwuXTK8+C1YJ9HZe6FUMnt27dp1aoVZ8+exd/fnx07dlCzZk21wxJCZFOWk5HatWuj0WjQaDS0bNkSB4eUU3U6HVevXqVtW/nL20Tq8vhNmqR7WGioodqqm1s2Kq6GjkrZdu8IugcQscDwXBcJPoMMvSPCRERcBDsu7yBJn2TRebeibmV+kMhVV65cISQkhKtXr1KiRAl27tzJM888o3ZYQogcyHIykjyL5uTJk7Rp08ZktUsnJyfKlClD9+7drR5gvnPrFly9ati+ds3wtUcPmDcv3VPc3Q2PbFHiwO8LCPsYYjYaHqkVGZHNFy7Y3tz0JstPL8/2+Y72jlaMRlhizpw5XL16lfLly7Nz507KlCmjdkhCiBzKcjIyYcIEAMqUKUPv3r1ljQdzQkOhXDlITDRtf6pSbfLAVasIXgmR6Qwcti+WdpVeAcDdx3cBqBZQjWLulk1hCnAPoH3F9rkRlsiCKVOmYGdnx+jRowkKkjovQhQEFo8ZGThwYG7EUTDcvGlIROztoUIFQ5ubG/TtazwkeeDq0+NFss3OfN0SHEpDuTNWukj+dz/mPo/jU2q+xCbFAvBJk0/o+WxPtcISWfTff//xzDPPYG9vj4ODg7HCqhCiYMhSMuLr68uFCxfw8/OjSJEiGa7xEB4ebrXg8pXYWPjhB8N2cDCcO2f2sKcHrmZ7Cm96PF6CgGngVNaKL5q/bTi/gc7LpTJwfrVz5066dOnCyy+/zPz587Gzs3gSoBDCxmUpGfn222+NRYS+/fZbWXDKnOXLYf58w7ara5ZOCQ3NxuwZc7wHgWdPsPezwosVPLP+mgWAi4MLjnYpYz2CvYJpULKBWmGJLFi3bh29evUiISGBO3fukJCQILeIhSiAspSMpL41M2jQoNyKJX979Chle/bs9I9Lxd3dSrmDnZvhIdK48/gOu67uAuD0m6cp71te5YhEVi1dupSBAwei0+no3r07S5cuxdnZWe2whBC5wOL+zuPHj3Pq1Cnj83Xr1tG1a1c+/PBDEhIK6UJsf/4J//ufYbtfPwgJUTceAcDsv2YTPD0YvaKnQckGkojkI/PmzaN///7odDoGDhzI8uXLJRERogCzOBl5/fXXuXDhAmCY79+7d2/c3NxYtWoVY8eOtXqA+cK0aSnbmSwuk05BVsvpHsLjdYby78KspaeWGrffev4tFSMRlvj222954403UBSFESNGsHDhQpO6RkKIgsfiZOTChQvUqlULgFWrVtG0aVN+/fVXFi1axG+//Wbt+PKH5Km8zZrBp5+me5iiQOPGObyWoof74+CiH0SvAzv5azEzc9rPoU/1PmqHIbKoUqVKODg4MG7cOGbNmiUDVoUoBLJVDl6vN6x9snPnTjp27AhAyZIlCQsLs250+U2fPpDOaqGKYlgQ7+RJw/NatbI5i+beMIhcaNiO3giXUi1jX0Gqg5pTwqtE5gcJm9GhQwdOnTpF5cqV1Q5FCJFHLP6To27dunzxxRcsWbKEffv20aFDBwCuXr1KsUxuURRWybVFUn97LFoIL1nidYhalvJc9wCSbqc8hMiHdDodY8eO5dKlS8Y2SUSEKFwsTkZmzJjB8ePHeeutt/joo4+o8KS41+rVq2nQQKZJmmOutki2yr8nXIYSGwx1RIQoABISEujXrx9Tp06lbdu2xMfLGCghCiOLb9PUqFHDZDZNsqlTp2Jvb2+VoAqyHNUWcWtuODH+v7T7Su3NaWhC5KnY2Fh69uzJpk2bcHR0ZMqUKTJjRohCKttD1I8dO8bZs2cBqFq1Ks8995zVgirIclRbJL0TS2wCt6bZjkmIvPb48WO6dOnCnj17cHFx4ffff5dVv4UoxCxORu7fv0/v3r3Zt28fPj4+AERERNC8eXOWL1+Ov7+/tWMUT3MIANdGhgSk6Diwy+6Sv0LkvfDwcNq3b8+ff/6Jp6cnGzdupEmTJmqHJYRQkcVjRkaOHEl0dDRnzpwhPDyc8PBwTp8+TVRUFG+//XZuxFh4aA/B9QZwzhHO2RkeFzxBe8D0OK/eUPoA+H8hiUg6wrRh/HHrD7XDEGaMHj2aP//8E19fX3bt2iWJiBDC8p6RrVu3snPnTqpUqWJsq1q1KnPmzKF169ZWDa5QSboLt9qCPjqlTeP25BZMTouTFD5Nfkr5gLPXyFgmWzJt2jTu3LnD9OnTqVatmtrhCCFsgMXJiF6vx9HRMU27o6Ojsf6IyIaYnaaJCIDGDh5Ogoeplkv3GQye3fI2tnzoZtRNAGoUq0GT0vKXt9oiIyPx9vYGoGjRomzfvl3liIQQtsTi2zQtWrTgnXfe4c6dO8a227dv8+6779KyZUurBleoOFWAohNSPcYbkpOYbRCzMeVh56F2pPnK771/x9PZfCE6kTeSC5jNnTtX7VCEEDbK4mRk9uzZREVFUaZMGcqXL0/58uUpW7YsUVFRzJo1KzdiLBxcXwT/Tw0Pv08g6V7aY9xagqvMmhH5x9GjR2nWrBn37t1j3rx5JCYvnSCEEKlYfJumZMmSHD9+nF27dhmn9lapUoWQwrBS7ZQp8MMPaVe7u2cmcciJuL/Azg2KvPOkwR5cnwePLrIWjcg39u3bR8eOHYmOjqZ+/fps2bLF7C1eIYSwKBlZsWIF69evJyEhgZYtWzJy5Mjciss2ffcd3LiR/v6KFbP+WkmhoN0L+kjDc+/BoHnyi9r1BcNDiHxq69atvPTSS8TFxdG8eXPWrVuHZzrrNgkhRJaTke+//54RI0ZQsWJFXF1dWbNmDZcvX2bq1KmZn1xQJPeI/PQTpJpNBBjKqpYrl+ZwrRZiYp56nehtcLsLKKlKX3v1S0lGhEW2XdrGu9veJS4pztgWnRCdwRkiN61evZq+ffuSmJhIx44dWbVqFS4uLmqHJYSwYVlORmbPns2ECROYMGECAL/88guvv/564UpGklWvDnXqZHhI8uJ4qdekMe64JZUmrWnpqaWcDTubpt3L2YsA9wAVIircLly4QGJiIr1792bJkiVya0YIkaksJyNXrlxh4MCBxud9+/Zl6NCh3L17l6CgoFwJLj97enE8MCyQ56adYP6E+H8Ng1iFxRQMPVYj642kX/V+xvbyvuXxcJLZR3lt3LhxPPPMM3Tt2lXWqxJCZEmWk5H4+HjcUy01a2dnh5OTE7GxsbkSmM05exZu3sz0MHO3ZkJDDWvSuLmB5uJM8yfGbJdkJIdKe5emfon6aodRKC1atIju3bvj6emJRqOhe/fuaockhMhHLBrAOn78eNzc3IzPExIS+PLLL43FjACmT59uvehsRWQk1K6d8tzB/LctvVsz7u6GBwBuIUDSkxOSIGazYduxrFVDFiIvKIrChx9+yFdffcXPP//Mjh07cEjn/4cQQqQny781mjRpwvnz503aGjRowJUrV4zPNdlejtbGPXoE8U8Gm77+umHMiBnp3ppxS9VQ4reU7cQbcKUiKAng0cm6MQuRy/R6PSNHjuS7774DoEOHDpKICCGyJcu/Ofbu3ZuLYdiwuDiYPNmw7eoKWawiaXJrxlyOpiRC2Ofg/SoUHQv2RawXsxC5LCkpiSFDhrBkyRI0Gg1z587ltddeUzssIUQ+JX/GZGbzZkOhMwAvryyfZnJrxhyNIwTNz1lsQqggPj6evn37smbNGuzt7Vm8eDF9+/ZVOywhRD4myUhmUo9EXbkye6+hJELsn5B0B1zqglO5zM8RwkYNGzaMNWvW4OTkxMqVK+nSpYvaIQkh8jlJRjLyxx8wYIBhu00baJKN1V8Tb8KN5pB42fA8cIEkI1YQGh3KpfBLANyPua9yNIXLmDFj2LNnDz/99FPhWAZCCJHrJBnJyKJFKdulSmXvNUJHpSQiwioi4yIp/3/liUk0LW1rp7F43UeRRYqiGAeo16hRg0uXLuHsLOskCSGsQ357Z0SnM3xt1gyysyKxokD0mrTt8WmrhYqsC40JJSYxBg0aKvpWpKJvReoH16djpY5qh1Yg3b17l/r163PgwAFjmyQiQghrylbPyIEDB5g3bx6XL19m9erVBAcHs2TJEsqWLUujRo2sHaP6QkIgO798E86kbYuYayhwFrwi53EVMkn6JBRFIVFnWIbe28WbCyMvqBxVwXbt2jVCQkK4fPkyr7/+OqdOnZKqqkIIq7O4Z+S3336jTZs2uLq6cuLECeKf1N+IjIxk0qRJVg8wX4s/l7Yt7m/QPcz7WPK5BccX4PqlK05fOFHt+2pqh1MonDt3jkaNGnH58mXKlSvHpk2bJBERQuQKi5ORL774grlz5zJ//nyTBbAaNmzI8ePHrRpcgeUhtxMstf3ydpL0SSZtzcs0Vymagu/kyZM0adKE27dvU7VqVQ4cOEDZslIlWAiROyy+TXP+/HmamJlV4u3tTUREhDViKjg8u0DFCMN2wnkI+xS8h4BnVxWDyt+mhEzhtTqG4lrezt6ZHC2y48iRI7Rr147IyEiee+45tm3bhp+fn9phCSEKMIuTkcDAQC5dukSZMmVM2g8ePEi5cjJl1YTGEeyffGC61oOSm9WNJ59aeGIhq/5bBYCboxs+Lj7qBlTAff/990RGRtKwYUM2bdpksvaUEELkBouTkWHDhvHOO++wcOFCNBoNd+7c4ciRI4wZM4bx48fnRoyikJt+JGXxxdLepVWMpHCYP38+ZcuWZezYsSYrdQshRG6xOBn54IMP0Ov1tGzZEq1WS5MmTXB2dmbMmDGMHDkyN2LMn6J+B4qAezO1I8n39IoegGmtp8n03Vzyxx9/UK9ePezs7HB2dmbixIlqhySEKEQsHsCq0Wj46KOPCA8P5/Tp0/zxxx88ePCAzz//PDfiy7/uvgLRG9SOokB5Lui5grsytIoWLFhAgwYNePfdd1EURe1whBCFULYrsDo5OVG1alVrxmI7PvsMDh6EM2bqhKRHUeDRQmBoroUlhLV9++23jB49GoCEhASTSqtCCJFXLE5GmjdvnuEvq927d+coINU9egQTJpi2BQVlfl74FLg/CbPJiPYwuDWwSnhCWIOiKHz22Wd8+umnAIwdO5avvvpKEhEhhCosTkZq1apl8jwxMZGTJ09y+vRpBg4caK241JOUqpbFL7+Aj49hkbzMRP4EQYuealQgfCZEr4NS+TxJEwWGoiiMGTOG6dMNA4O/+OILPvzwQ0lEhBCqsTgZ+fbbb822f/rpp0RHR+c4IJvSr1/Wj/WbBPbdTNsePfleeQ+zXkxC5NDIkSOZM2cOADNnzuTtt99WOSIhRGFntYXyXnnlFRYuXGitl8t/vLqbb9c4g/egPA1FiIw0bdoUR0dHFi5cKImIEMImZHsA69OOHDmCi4uLtV6uYPDsDkVGy3iRbIqIi+BW1C3ikuLUDqVA6dmzJ/Xr16dUqVJqhyKEEEA2kpFu3UxvRSiKwt27d/n777+l6FlqFW6BVxG1o8i3IuIiKPVtKR4nPFY7lHwvOjqakSNH8tlnn1GyZEkASUSEEDbF4ts03t7eJg9fX1+aNWvG5s2bmfD0LJQsmjNnDmXKlMHFxYX69evz119/Zem85cuXo9Fo6Nq1a7aum6vsJRHJiZuRN3mc8BgNGgLcA3ihxAvULV5X7bDynYiICFq3bs2iRYt46aWXpI6IEMImWdQzotPpGDx4MNWrV6dIEet82K5YsYLRo0czd+5c6tevz4wZM2jTpg3nz58nICAg3fOuXbvGmDFjaNy4sVXiELYpwD2Ae2PuqR1GvnT//n3atGnDyZMnKVKkCHPmzJEZM0IIm2RRz4i9vT2tW7e26uq806dPZ9iwYQwePJiqVasyd+5c3NzcMhwMq9Pp6NevHxMnTpTF+YQw49atWzRp0oSTJ09SrFgx9u7dS/369dUOSwghzLL4Nk21atW4cuWKVS6ekJDAsWPHCAkJSQnIzo6QkBCOHDmS7nmfffYZAQEBDB2aebXT+Ph4oqKiTB5WE3ccIn+FR99B1AqIWmu91xYimy5fvkzjxo05f/48JUuWZP/+/dSoUUPtsIQQIl0WD2D94osvGDNmDJ9//jl16tRJs6qnl5dXll8rLCwMnU5HsWLFTNqLFSvGuXPnzJ5z8OBBfvzxR06ePJmla0yePDl3Fv3Sx8G1OqZtWjcgxvrXEsICw4cP59q1a1SoUIFdu3bJYFUhhM3LcjLy2Wef8b///Y/27dsD0LlzZ5P7z8lrWuh0OutH+cTjx4/p378/8+fPx8/PL0vnjBs3zrj2BkBUVJRxRkGOPBiX89cQJvZd28eXB74kQZdAdEIBK6CXh37++WeGDx/Od999R2BgoNrhCCFEprKcjEycOJE33niDPXv2WO3ifn5+2NvbExoaatIeGhpq9pfo5cuXuXbtGp06dTK26fWG5eUdHBw4f/485cuXNznH2dkZZ2dnq8VsFLPF+q9ZyP3fX//Hjis7TNqKexZXKZr85f79+8YB34GBgaxZs0bliIQQIuuynIwkTwls2rSp1S7u5OREnTp12LVrl3F6rl6vZ9euXbz11ltpjq9cuTKnTp0yafv44495/PgxM2fOtE6PR5ZpwM435ak+PA+vXTAl6Q3rAr1e53Valm2JRqOhUalGKkdl+7Zt20b37t35/vvv6d+/v9rhCCGExSwaM5Ib0wJHjx7NwIEDqVu3LvXq1WPGjBnExMQwePBgAAYMGEBwcDCTJ0/GxcWFatWqmZzv4+MDkKY915U7m7IdewRutgE7t7yNoQD56/ZfrD+/HoC6xevS89meKkeUP6xZs4aXX36ZxMREVq1axSuvvCLTd4UQ+Y5FyUilSpUy/UUXHm5ZD0Hv3r158OABn3zyCffu3aNWrVps3brVOKj1xo0b2NlZbQkd61N0EHcSxf//iNG2VTuafOu1Da8Ztz2dPFWMJP9YsmQJgwcPRqfT0bNnT3755RdJRIQQ+ZJGyWJJRjs7O2bMmIG3t3eGxw0cONAqgeWWqKgovL29iYyMND/z58EDSC62lsVqlYoCjRrB4cMpbdHR8NREI5GB8v9XniuPrvBS5ZdY2m0pro6uaodk07777jtGjBgBwODBg5k/fz729vYqRyWEKOgy/QzNJot6Rl5++eUMq6IWVlqtaSLSsCG4yR2bLLv66CpXHhlq17zX4D1JRDLx9ddf8/777wPw9ttv8+2339p276EQQmQiy7/BpPsXQxdI+Cy4Nxz0sWYPCQ2FAwdAvl1Zo9PrqLegnvG5g53VFpIusB49egQYBm/PmDFDEhEhRL5n8WyaQi18CkT+BEGLwc78X+/u7pKIWCJBl0CYNgyAnlV7UjuotsoR2b5JkybRokULWrVqpXYoQghhFVlORpLreRRqyYXOwj4Fl+eeNCrgOhFwVCmo/Omh9iHaRC1xSXHGth87/yg9I2YkJSUxY8YMRowYgaurKxqNRhIRIUSBIr/5syrxdsp2zFbD4wkl+EsVAsq/lp9eTt/f+qIgvW2ZSUhIoF+/fqxevZoDBw6wdu1auWUqhChwJBnJqtj9ZpsVBRo3kQ8HS/x9528UFOw0djjaGXqUQsqF4OHkoXJktkWr1dKjRw+2bNmCk5MTgwYNkkRECFEgSTKSVXrzC+BpXT8lec2+WrVkFo0lxrw4himtpqgdhk2KioqiU6dO7N+/H1dXV9auXUvr1q3VDksIIXKFJCM55dnNuCmzaExdDr/MSyte4oH2gUl7VHyUShHlDw8fPqRdu3YcPXoULy8vNm3aRKNGUhZfCFFwSTKSVZ7dwbWBYTvhPCiJ4PwsJFQwHiKJiKmdV3Zy6v6pdPc/G/BsHkaTPyiKQvfu3Tl69ChFixZl27Zt1KlTR+2whBAiV0kyklX2RQwPAOeqKe1J6oSTn7Qo24Jv23xr0ubp5EnZImVVish2aTQavv76awYMGMCaNWuoWrVq5icJIUQ+J8mIsLr91/cz88+ZXAq/BIC3szc1itVQOSrblpSUhIOD4b9jvXr1OH36tPG5EEIUdFK6MSMJV+BmR7hqvhCXokCM+XGthdqXB75kzdk1/Bv6LwAB7rKEQEb+/fdfqlatyt9//21sk0RECFGYyG+8jNxoDkk3wLFMml3mFscTBgm6BABee+41GpVqRKdnOqkcke36888/adu2LREREYwbN44dO3aoHZIQQuQ5SUYyknQjbZuSCBpHWRwvC1qWa0mvZ3upHYbN2rt3L506dSI6OpoXX3yRVatWqR2SEEKoQm7TZEXiTcPX8G8hNm1XiCyOJyy1adMm2rVrR3R0NC1btmT79u34+PioHZYQQqhCkpGscCxpSETujza7WxbHE5ZYuXIlXbt2JS4ujs6dO7Nx40Y8PKT6rBCi8JJkJCsSr6WbiAhhCUVRWLJkCUlJSfTp04fVq1fj4uKidlhCCKEqSUYs5RAMGAawCmEpjUbDihUrmDZtGkuWLMHRUVZ7FkIISUZSi4qCPn1SnvuOffJ4L6XNsbxhcbzGeR+eyJ8URWHbtm0oTzJYNzc3Ro8ejb29vcqRCSGEbZBkJLU9e2DXLsN28eIQMMXwcCgJ3q9CxYeg0aDVIovjPaFN1BIZF2nySNJLWdpkiqLw/vvv07ZtW8aPH692OEIIYZNkam9qSak+RI8cSdn2HZnuKYV5Fs0v//7CoLWD0Ck6tUOxSXq9nhEjRjB37lwA/Pz8VI5ICCFskyQj5jRuDKVKZenQwpqIgKHse3qJiJ+bH3WL183jiGxHUlISgwYNYunSpWg0Gn744QdeffVVtcMSQgibJMmIyLFPm37KB40+MGlzsHPA3q5wjomIj4/n5ZdfZu3atTg4OPDLL7/Qu3dvtcMSQgibJcmIsNhn+z7j11O/cjf6LmBIPJwdnFWOyjYoikLXrl3ZunUrzs7OrF69mo4dO6odlhBC2DQZwJpFyYviJT8Ks2lHpnH+4Xmi4qMAKO9bXuWIbIdGo6F37954enqyefNmSUSEECILpGfEnMSbEDbJ+FRxqkajdp1lUbwnkqeoLuu+jGf9n6VaQDWVI7ItgwYNokOHDvj7+6sdihBC5AvSM2JO4jUI+8j40N5fbzYRKeyL4z1f/HmqF6uOpjCP4gXu3LlD165dCQ0NNbZJIiKEEFknPSMWCg01rEUDhkSkkH8OF3pXr14lJCSEK1eukJiYyKZNm9QOSQgh8h1JRizk7p6SjIjC7ezZs4SEhHDnzh3Kly/PnDlz1A5JCCHyJUlGRKbik+JZ/d9qHsU9AiBBl6ByROo7ceIErVu3JiwsjGeffZYdO3YQFBSkdlhCCJEvSTIiMrX01FKGrh+apr2wTuc9dOgQHTp0IDIykrp167J161aKFi2qdlhCCJFvSTJijusLUGFDynNt4fzQTRamDQOgtHdp6peoD8Bzgc9RwquEmmGpQq/X89ZbbxEZGUnjxo3ZuHEjXl5eaoclhBD5miQj5mgcwSHVOiIFtJBodEI0/z34L9PjbkTeAKBZmWYs6rool6OybXZ2dqxdu5bPPvuMWbNm4VaYp1MJIYSVSDJSiD037zkuhl/M8vGFeQrv9evXKV26NAClS5fmxx9/VDkiIYQoOCQZKcSSE5ESXiWw12Tc/ePq6Erfan3zIiybM2/ePEaOHMmyZcvo3r272uEIIUSBI8lIBhQFtNqCX/79+GvH8XeXIl3mTJ06lbFjxwJw8OBBSUaEECIXSAXWdCgKNGoEHh5QrJja0Yi8pigKn3zyiTER+eCDD5g+fbrKUQkhRMEkPSPp0GpJUwK+sJd/LywURWH06NHMmDEDgEmTJjFu3Dh1gxJCiAJMkpEsSC4BL+XfCz69Xs9rr71mHKA6a9Ys3nrrLZWjEkKIgk1u06Smf/zkazRK7D/GZndXLe7ukogUBhqNBldXV+zs7Fi0aJEkIkIIkQckGUkt9gQASuwJGjfRp7QnnFcpIJHXNBoNM2fO5MiRIwwcOFDtcIQQolCQZMQMrd6Nk2drA1Crygnc3PSZnCHys8ePHzN+/HgSEgxr7tjZ2VGvXj2VoxJCiMJDxoxk4sCSxmg0+9QOQ+SS8PBw2rVrx19//cXt27dZuHCh2iEJIUShI8nIUxQgRu9ufK7RKOoFI3JVaGgorVu35t9//8XX15fhw4erHZIQQhRKcpsmFUWBRhyk2L/31Q5F5LIbN27QuHFj/v33XwIDA9m3bx9169ZVOywhhCiUpGckFS3PcpiGxucNX7iPW7nvwbGMekEJq7t48SIhISHcuHGD0qVLs3PnTipUqKB2WEIIUWhJMpKaQ4BxMzQU/P0D0GgGqBiQsLakpCQ6duzIjRs3qFSpEjt37qRkyZJqhyWEEIWa3KZJh9QVKZgcHBz44YcfeOGFF9i/f78kIkIIYQMkGUmWmAi7d6sdRZ55HP9Y7RDyVFxcnHG7adOmHD58mGKy6JAQQtgESUaSzZgBC39UO4o8ERkXSakZpdQOI89s3bqVihUrcvr0aWObRrq9hBDCZkgykuz2bbUjyDM3o24SERcBQOdnOuPn5qduQLlo9erVdO7cmVu3bsmqu0IIYaMkGSnEAtwDWPfyugLbS7Bo0SJ69+5NYmIivXv3Zt68eWqHJIQQwgxJRkSBNHv2bAYPHoxer2fo0KEsXboUR0dHtcMSQghhhiQj6UkKVTsCkU2TJ09m5MiRAIwaNYr58+djb2+vclRCCCHSI3VGUlFIdbsi8hdISjRsew8Ch0BVYhKWSUxMZNu2bQB88sknfPrppwX2NpQQQhQUkow8oSjQmAMpDWGfgFZr2HZvJclIPuHo6Mj69etZu3YtAwZIwTohhMgP5DbNE9okJ05SG4BaVU7g5qpN2en0jEpRiaxISkrit99+Mz738vKSREQIIfIRSUbMOLCksWn1VTsP1WIRGYuPj6d379706NGDr7/+Wu1whBBCZIPcpjFDo1FSnrg2TP9AoSqtVku3bt3Ytm0bTk5OPPOM9GAJIUR+JMmIOZ49wS3esC23aGxSZGQknTp14sCBA7i5ubFu3TpCQkLUDksIIUQ2SDJiTvFF4K52ECI9YWFhtG3blmPHjuHt7c3mzZtp0KCB2mEJIYTIJklGChlFUTh6+6jaYWRbfHw8zZs35/Tp0/j5+bF9+3Zq166tdlhCCCFyQAawFjKHbh5iyPohANhr8l8hMGdnZ9544w2Cg4M5cOCAJCJCCFEASDJSyNyKumXcHt9kvIqRZN+IESP477//qFy5stqhCCGEsAJJRgqomIQY7kXfS/NIXq23RdkWvPn8m+oGmUX//PMPrVq1Ijw83Njm5eWlYkRCCCGsScaMJFMSUm3ryc952j/3/uHFH18kNilW7VBy7MiRI7Rv356IiAjGjh3LggUL1A5JCCGEleXfT1xr0j2Gx+tSnmvy97fl5L2TxkTETmOX5uFk70TnSp1VjjJzu3btolWrVkRERNCwYUOmTZumdkhCCCFygfSMAERvAF1kynPdY9ABGk2+rr7arkI7NvfbrHYY2bJhwwZ69uxJfHw8rVu3Zs2aNbi7y3xrIYQoiPJ3F4C1TJ8Py+JTnl8KhItecO0F9WLKpiM3jzBo3SC1w8iR5cuX061bN+Lj43nppZdYv369JCJCCFGASTICsOak+XbP7nkahjX8fu534/YzRfNf9di4uDjGjRtHUlISr7zyCitXrsTZ2VntsIQQQuQiSUYAdBHm24uOy9MwrEFRDOvqtCrXimlt8t8YCxcXF7Zv3864ceP4+eefcXCQO4lCCFHQyW/6jGjyx7dnzl9z2Ht9L2CYSQNQs1hN7PLJQFxFUTh79ixVq1YFoGLFikyaNEnlqIQQQuSV/PFpm9ucnwFupjz36gea3fkiGdEmahm5ZSQKikm7v7u/ShFZRlEUxowZw6xZs1i/fj1t27ZVOyQhhBB5zPY/bXPbiRNw8jzgltKmPQCVt6gWkiWS9EnGRGRGmxk42jvi6eRJ96q2P95Fp9PxxhtvGGuHXLp0SbVY9Ho9CQkJmR8ohBAFnJOTE3Z2eduzXriTkcREaNo0bXu50+Ck7rot4bHhLDu1DG2iNsPj4pLijNtv1H0DZ4f8MdgzMTGRgQMHsmzZMuzs7Jg/fz5DhgxRJZaEhASuXr2KXq9X5fpCCGFL7OzsKFu2LE5OTnl2zcKdjCQkwOPHhu0hQ2Hhk3YbWEDu60NfM+XQlCwf72jnmG/GiMTFxdGrVy82bNiAg4MDS5cupVevXqrEoigKd+/exd7enpIlS+b5XwNCCGFL9Ho9d+7c4e7du5QqVQqNRpMn1y3cyUhqX32VkozYgEexjwCoFViLmsVqZnp8y7ItcbR3zO2wciw2NpZOnTqxa9cuXFxcWL16NR06dFAtnqSkJLRaLcWLF8fNzS3zE4QQooDz9/fnzp07JCUl4eiYN58rNpGMzJkzh6lTp3Lv3j1q1qzJrFmzqFevntlj58+fz+LFizl9+jQAderUYdKkSekenx8l6BI4ePMgAN2rdOfjJh+rHJH1ODs7U7JkSTw8PNiwYQPNmjVTNR6dTgeQp92RQghhy5J/H+p0ujxLRlTvk16xYgWjR49mwoQJHD9+nJo1a9KmTRvu379v9vi9e/fSp08f9uzZw5EjRyhZsiStW7fm9u3beRx57hm6fij/PfgPIN/cesmq5PEhR48eVT0RSS2vuiKFEMLWqfH7UPVPuunTpzNs2DAGDx5M1apVmTt3Lm5ubixcaP6eydKlSxk+fDi1atWicuXKLFiwAL1ez65du/I48txz5dEV43bXyl3VC8RKbt26xZgxY0hKSgLAwcGBypUrqxyVEEIIW6FqMpKQkMCxY8cICQkxttnZ2RESEsKRI0ey9BparZbExER8fX3N7o+PjycqKsrkYRSzN2X7crlU7bsteRu55vfev1PVv6raYeTI5cuXadSoEdOmTePDDz9UO5xCbe/evWg0GiIiItQORYhMjR8/ntdee03tMAqcrVu3UqtWLZubPahqMhIWFoZOp6NYsWIm7cWKFePevXtZeo3333+f4sWLmyQ0qU2ePBlvb2/jo2TJkqn26lI29Y9TtpVU7SLbzpw5Q+PGjbl+/ToVKlTgrbfeUjukQq1BgwbcvXsXb29vtUMpNDQajfHh5eXF888/z7p169IcFxsby4QJE6hUqRLOzs74+fnRs2dPzpw5k+bYqKgoPvroIypXroyLiwuBgYGEhISwZs0a43IQ+d29e/eYOXMmH330kdqh5Jrw8HD69euHl5cXPj4+DB06lOjo6AzPuXfvHv379ycwMBB3d3eee+45fvvtN+P+a9euMXToUMqWLYurqyvly5dnwoQJJjWU2rZti6OjI0uXLs2195Ydqt+myYmvvvqK5cuX8/vvv+Pi4mL2mHHjxhEZGWl83Lx50+xxwrqOHTtG06ZNuXv3LtWrV+fAgQOUKlVK7bAKNScnJwIDA7N9Pzi/FYVTFMV4a1BNP/30E3fv3uXvv/+mYcOG9OjRg1OnThn3x8fHExISwsKFC/niiy+4cOECmzdvJikpifr16/PHH38Yj42IiKBBgwYsXryYcePGcfz4cfbv30/v3r0ZO3YskZGRefa+EhMTc+21FyxYQIMGDShdunSOXic3Y8ypfv36cebMGXbs2MHGjRvZv39/pj1BAwYM4Pz586xfv55Tp07RrVs3evXqxYkTJwA49//t3Xlczdn/B/DXbbvdllsqqYgsbaJStgoVUWOZzAxCsozBkGWMZQxGjH3sjGWYFL4RxliGYkKRNDQqW5soWcrSXkp17/v3R78+4+oWUV3VeT4e9zHu+Zzz+bw/p+bed5/POZ+TkACxWIzffvsNd+/exaZNm7Br165KV6XHjx+PrVu31tm5fRCSodevX5O8vDwdP35conzs2LH0+eefV9t23bp1pKGhQVFRUTU6Zm5uLgGg3NxcomcniQAigApujav4JxVk/F3TU6lV9r72hKWg4/HHZRrHh7p8+TKpq6sTAOrevTtlZmbKOqQqFRUVUVxcHBUVFUluSOlZ+ZW5+d07fBUpve2ryFqN29HRkaZPn06zZs0iTU1N0tXVpd27d1NBQQGNHz+e1NTUqH379hQUFMS1CQ0NJQCUnZ3NlV25coUcHR1JIBCQpqYmDRgwgLKysrhjeHt706xZs0hbW5ucnJyIiCgsLIy6detGSkpKpKenRz/88AOVlpZWG+/169fJxcWFtLW1SSgUUp8+fejGjRvc9lGjRtGIESMk2pSUlJC2tjbt27ePiIhEIhGtWrWKjIyMSFlZmSwtLeno0aOVzi8oKIhsbGxIUVGRQkNDKTk5mT7//HPS1dUlVVVV6tq1K4WEhEgc6+nTpzRw4EBSVlYmIyMjCggIoDZt2tCmTZu4OtnZ2TRx4kTS0dEhdXV1cnZ2ptjY2GrPG4DE51teXh4BoC1btnBla9asIR6PV2lfIpGIunbtSh07diSxWExERFOnTiVVVVV68uRJpWPl5+dX+3M4deoUde3alfh8Pmlra9PQoUOrjJOISENDg/z8/IiIKCUlhQBQYGAg9enTh/h8Pm3ZsoWUlZUlfseIiP78809SU1OjwsJCIiJKS0uj4cOHk4aGBjVr1ow+//xzSklJqTJOIiILCwv69ddfJcqCg4PJwcGBNDQ0SEtLiwYNGkTJycncdmkxVsS/Z88eMjMzIz6fT6amprR9+3aJfc+fP5+MjY1JIBBQ27ZtafHixVRSUlJtjB8jLi6OAEh8fwUHBxOPx5P6s62gqqpK+/fvlyjT0tKiPXv2VNnml19+obZt20qUPXz4kABI9N+bqvxcpLe+Q2uRTJMRIqLu3bvT9OnTufcikYhatmxJq1evrrLN2rVrSSgUUmRkzT/gq05GJtV7MhKWEkbddnejTjs6SbwEKwQNNhnJz88nbW1tAkCOjo6Ul5cn65CqVeX/dPGo/MqY/e4d5p+V3jb/bK3G7ejoSOrq6rR8+XJKSkqi5cuXk7y8PH322We0e/duSkpKoqlTp5K2tjb3pfB2MhITE0N8Pp+mTp1KsbGxdOfOHdq2bRu9ePGCO4aamhrNmzePEhISKCEhgR4/fkwqKio0bdo0io+Pp+PHj5OOjg75+PhUG++FCxfowIEDFB8fT3FxcTRx4kRq0aIF9/tx+vRpEggElJ+fz7X566+/SCAQcHVWrFhBZmZmdPbsWbp//z75+fkRn8+nsLAwifOztLSkv//+m5KTkykzM5NiY2Np165ddPv2bUpKSqLFixeTsrIyPXz4kDuWi4sLWVtb0z///EM3btzgErQ3kxEXFxcaMmQIRUVFUVJSEs2ZM4e0tbWrTbbf/JIvLS2lTZs2EQDauXMnV8fS0pIGDBggtX1AQAABoJiYGBKJRNSsWTOaPHlytX0tzenTp0leXp6WLFlCcXFxFBsbS6tWrZIaZwVpyYiRkREdO3aMHjx4QE+fPqVhw4bRmDFjJNp99dVXXFlJSQmZm5vT119/Tbdu3aK4uDgaPXo0mZqa0uvXr6XGmpmZSTwej/755x+J8j/++IOOHTtG9+7do5iYGBoyZAh17tyZRCJRtTH+73//I319fa7s2LFjpKWlRf7+/ty+ly9fThEREZSSkkKnTp2iFi1a0Nq1a6vt044dO5KqqmqVLzc3tyrb+vr6kqampkRZaWkpycvL059//lllu/79+9OgQYMoMzOTRCIRHTp0iFRUVOjevXtVtlm0aBHZ2tpWKm/RogX3831bk0xGAgMDic/nk7+/P8XFxdHkyZNJU1OTMjIyiIjIy8uLFixYwNVfs2YNKSkp0R9//EHp6enc680PsepIdGT2PenJyLNLdXKub5twYgJhKap8xaTH1EsctS04OJiGDh1Kr169knUo79SQk5FevXpx78vKykhVVZW8vLy4svT0dALAJe1vJyOjRo0iBweHao/RpUsXibKFCxeSqakp95c6EdH27dtJTU2N+1J4HyKRiNTV1emvv/4iovIPYh0dHYm/+kaNGkUeHh5ERFRcXEwqKip09epVif1MnDiRRo0aJXF+J06ceOfxLSwsaNu2bUREFB8fX+mv1Hv37hEALhkJDw8noVBIxcXFEvtp3749/fbbb1UeBwApKyuTqqoqycnJcV+WbyYwysrKNGvWLKnto6OjCQAdPnyYnj17RgBo48aN7zy/t9nZ2ZGnp2e1cb5PMrJ5s+TVwePHj0tcBcnNzSVlZWUKDg4mIqIDBw5U+n15/fo1CQQCOnfunNRYYmJiCAClpaVVe04vXrwgAHT79u1qY2zfvj0dPHhQomz58uVkZ2dX5b7XrVsn9Qv8TampqXTv3r0qX48fP66y7cqVK8nExKRSefPmzWnHjh1VtsvOzqYBAwYQAFJQUCChUFhlPxKV/x4LhULavXt3pW1dunShpUuXSm0ni2RE5g898/DwwIsXL7BkyRJkZGTA2toaZ8+e5Qa1pqWlSTyie+fOnSgpKcGwYcMk9uPj44OlS5fW7OCK+tLLBV1rtp8PJKby0cyTbCbBw8JDYltLYUuY6TSc6a/5+flQV1cHUD5Aiq2+W/csLS25f8vLy0NbWxudO3fmyir+H6rqmT2xsbEYPnx4tcewtbWVeB8fHw87OzuJcScODg4oKCjA48ePAQAdO/43A2zhwoVYuHAhnj17hsWLFyMsLAzPnz+HSCTCq1evkJaWBqB8uveIESMQEBAALy8vFBYW4uTJkwgMDARQvojiq1ev0L9/f4l4SkpK0KVLF4myrl0l//8tKCjA0qVLcebMGaSnp6OsrAxFRUXcsRMTE6GgoAAbGxuuTYcOHdCsWTPu/c2bN1FQUABtbW2JfRcVFeH+/fvV9uGmTZvg4uKCBw8eYPbs2di6dWul2X/0HgNP36dOVWJjYzFp0qQPbl/h7b4dOHAgFBUVcerUKYwcORLHjh2DUCjkJhTcvHkTycnJ3GdDheLi4ir7raioCAAqjQO8d+8elixZgmvXruHly5fcbJC0tDR06tRJaoyFhYW4f/8+Jk6cKHH+ZWVlEgO5Dx8+jK1bt+L+/fsoKChAWVkZhEJhtX3xseNZPsRPP/2EnJwcnD9/Hjo6Ojhx4gRGjBiB8PBwif/3AeDJkydwc3PD8OHDpf7sBQIBXr2qfu2z+iTzZAQApk+fXuVMi7CwMIn3qampdR+QXO0+FvxF4QusubIGOcU5EuURjyIAAMZaxujXrl+tHrM+7dixAytWrMClS5dgbGws63CajLefjMjj8STKKhKGqqbwCQSCdx5DVVW1RjEZGBggNjaWe1/xpTtu3DhkZmZiy5YtaNOmDfh8Puzs7CQGxXp6esLR0RHPnz9HSEgIBAIBl9RWzDI4c+YMWrZsKXFMPl9ycci3Y547dy5CQkKwfv16dOjQAQKBAMOGDavRgNyCggLo6+tX+jwCAE1NzWrb6unpoUOHDujQoQP8/PwwcOBAxMXFQVdXFwBgYmKC+Ph4qW0ryk1MTNC8eXNoamoiISHhveOu8K6fNY/Hq5TsSBv8+XbfKikpYdiwYTh48CBGjhyJgwcPwsPDAwoK5V8tBQUFsLW1lTpzo3nz5lJj0dHRAQBkZ2dL1BkyZAjatGmDPXv2wMDAAGKxGJ06dar0c3wzxorfmz179qBHjx4S9eTly9cgi4yMhKenJ5YtWwZXV1doaGggMDAQGzZskBpfBQsLCzx8+LDK7b1790ZwsPTV3/X09Cr9kVBWVoasrCzo6elJbXP//n38+uuvuHPnDiwsLAAAVlZWCA8Px/bt27Fr1y6u7tOnT+Hs7Ax7e3vs3r1b6v6ysrKq/BnIwieRjHwStGbV2a4P3DqAjf9srHJ7M0GzKrd96tauXYsFCxYAAA4dOoQlS5bIOKJaotyzcpnie/wlJK8hva38pzed1tLSEhcuXMCyZcveu425uTmOHTsGIuKSnYiICKirq6NVq1aQk5NDhw4dKrWLiIjAjh07MHDgQADAo0eP8PLlS4k69vb2MDQ0xOHDhxEcHIzhw4dzyVXHjh3B5/ORlpYGR2krbVcjIiIC48ePxxdffAGg/AvqzT9qTE1NUVZWhpiYGO5KUHJyMrKzs7k6NjY2yMjIgIKCAoyMjGp0/Dd1794dtra2WLlyJbZs2QIAGDlyJBYtWoSbN2/Cyuq/dajEYjE2bdqEjh07wsrKCjweDyNHjsSBAwfg4+MDAwMDiX0XFBRAWVmZSwTeVPGznjBhgtS4mjdvjvT0dO79vXv33vuvZk9PT/Tv3x93797FxYsXsWLFCm6bjY0NDh8+DF1d3XdeaajQvn17CIVCxMXFwcTEBACQmZmJxMRE7NmzB7179wYAXLly5Z37atGiBQwMDPDgwQN4enpKrXP16lW0adNGYhpxdUlGhaCgoGpn61SXANrZ2SEnJwc3btzgfucuXrwIsVhcKWmqUPHzeHsxT3l5eYk/OJ48eQJnZ2fY2trCz89P6uKfFVem3r6qKFO1etOnAZC431VQ8N+YkeeF/40ZKajdY664tIKwFNR1d1dadXmVxGtX1C4qLCms3QPWA7FYTAsXLiQABIAWLVokcV+4oaju3uinzNHRsdI4g7dnfxBJjgV4e8xIYmIiKSkp0dSpU+nmzZsUHx9PO3bskBjA+vYxKgawent7U3x8PJ04ceK9BrB26dKF+vfvT3FxcfTPP/9Q7969Kw0QJSofbNexY0dSUFCg8PDwStu0tbXJ39+fkpOT6caNG7R161ZuIKK02UJERF988QVZW1tTTEwMxcbG0pAhQ0hdXV3i3FxcXMjGxoauXbtG0dHR5OzsTAKBgBt/IBaLqVevXmRlZUXnzp2jlJQUioiIoIULF1Y7ow9SxmIEBQURn8/nxhQUFRVRjx49yNDQkI4cOUIPHz6k69ev09ChQ0lVVVVioH5mZiaZmZlRq1ataN++fXT37l1KSkoiX19f6tChQ6VzrxAaGkpycnLcANZbt27RmjVruO0jR44kc3Nzio6OpqioKOrbty8pKipWGjMSExNTad9isZgMDQ3JysqK2rdvL7GtsLCQjI2NycnJiS5fvkwPHjyg0NBQmjFjBj169KjKfvvyyy9pzpw53HuRSETa2to0ZswYunfvHl24cIG6desm0b9Vxbhnzx4SCAS0ZcsWSkxMpFu3btHevXtpw4YNRER08uRJUlBQoEOHDlFycjJt2bKFtLS0SENDo8r4aoObmxt16dKFrl27RleuXCFjY2Nu/BNR+f9rpqamdO3aNSIqHwzcoUMH6t27N127do2Sk5Np/fr1xOPx6MyZM1ybDh06UL9+/ejx48cS4yrfFBoaKjHW521NcgBrfavPZORR7iPac2MPuR9yJywFTTo1qXZ2LGMikYhmzJjBJSLvGnX+KWvKyQhR+TRde3t74vP5pKmpSa6urtx2aceoaFPTqb3R0dHUtWtXUlZWJmNjYzp69KjUeCumPLZp06ZScisWi2nz5s1kampKioqK1Lx5c3J1daVLly5VeX5E5V9SFcmFoaEh/frrr5XO7enTp/TZZ58Rn8+nNm3a0MGDB0lXV5d27drF1cnLy6MZM2aQgYEBKSoqkqGhIXl6elY70FJaMiIWi8nMzIymTp3KlRUWFtKiRYuoQ4cOpKioSFpaWvTVV19xgzPflJOTQwsWLCBjY2NSUlKiFi1akIuLCx0/frzaPwiOHTtG1tbWpKSkRDo6OvTll19y2548eUIDBgwgVVVVMjY2pqCgIKkDWKUlI0TlU2MB0JIlSyptS09Pp7Fjx5KOjg7x+Xxq164dTZo0qdovs6CgIGrZsqXEoOiQkBAyNzcnPp9PlpaWFBYW9l7JCFH5rKSKc2/WrBn16dNHYtbKvHnzSFtbm9TU1MjDw4M2bdpU58lIZmYmjRo1itTU1EgoFNKECRMkJmJUnE9oaChXlpSURF9++SXp6uqSiooKWVpaSgz69vPz4z6X3369afLkyTRlypQqY5NFMsIjaiSP7HtPeXl50NDQQG5uLoS8UkBYfn+y8Hkh1HTLx4oUFAA1vFUuVd99fRGaGsq99+7mjV8H/vrxO5YhkUiEb775Bv7+/gDKx4tMnTpVtkF9hOLiYqSkpKBt27ZVPjiPaXoeP34MQ0NDnD9/Hv36NdzxXA0VEaFHjx6YPXs2Ro0aJetwGpWXL1/C1NQU//77L9q2bSu1TnWfixLfoe956+19NN0xI9m/A3nb6vQQL169AAA4GDqgjWYbeHfzrtPj1YeioiLExcVBXl4efn5+8PLyknVIDPPRLl68iIKCAnTu3Bnp6emYP38+jIyM0KdPH1mH1iTxeDzs3r1b4km1TO1ITU3Fjh07qkxEZKXpJiMv5kg+DD/ZEEBmnRzqZ+ef0bdt3zrZd31TU1NDcHAwrl+/zqbvMo1GaWkpFi5ciAcPHkBdXR329vYICAioNGOJqT/W1tawtraWdRiNTteuXStN0f4UNN1k5G1ULOsIPll5eXn466+/uNHoWlpaLBFhGhVXV1e4urrKOgyGabKadDJCBLxC+TiRwqJaGCTSCGVlZcHNzQ1RUVHIz8/Ht99+K+uQGIZhmEamQa/a+zGIgF4Tr0ANhVBDIVq4SH9KZVOWkZEBR0dHREVFQVtbG927d5d1SAzDMEwj1GSvjLwqEuDqTYdK5Q4OgEotPIDV4w8P3Hl+5+N3JCMPHz6Ei4sLkpOToa+vj/Pnz0s85pthGIZhakuTTUao9WXu38+gC9VHfwOaFlBRVcQby258sCN3j3D/Ntcx//gd1qOkpCS4uLjg0aNHMDIywoULF9CuXTtZh8UwDMM0Uk02GXH73IT7tyoKoaplAqjU/sj5lFkp0FevYkG+T1B2djb69OmDZ8+ewczMDOfPn6+0FgjDMAzD1KYmO2akYvq6NWKggrpbuVBFsXYX3atrzZo1w+zZs2FtbY1Lly6xRIRhGIapc002GakQjt6ohbsyDd6bD+L94YcfEBkZya0qyjB1icfj4cSJE7IOg2EYGWryyQgPBGhpAUpKsg5FZs6cOQMnJyfk5+dzZezR6A1DZGQk5OXlMWjQIJnGkZCQAB6Ph3/++UeivGfPnlBWVkZx8X/P8SkuLoaysjJ8fX0BAOnp6fjss89qJY7Vq1dDXl4e69atq7Rt6dKlUh+ilZqaCh6Ph9jYWK6MiLB792706NEDampq0NTURNeuXbF58+b3Xs32Q6SlpWHQoEFQUVGBrq4u5s2bh7KysmrbJCUlwd3dHTo6OhAKhejVqxdCQ/9bhsLf3x88Hk/q6+1l7BlGVpp8MgIA+OcfQMqy203BkSNHMHToUFy+fBnr16+XdThMDfn6+mLGjBm4fPkynj59KrM4zMzMoKenh7CwMK4sPz8f0dHRaN68uUSSEhkZidevX6Nv3/KnEuvp6YHP59dKHHv37sX8+fOxd+/ej9qPl5cXvvvuO7i7uyM0NBSxsbH46aefcPLkSfz999+1EuvbRCIRBg0ahJKSEly9ehX79u2Dv78/lixZUm27wYMHo6ysDBcvXsSNGzdgZWWFwYMHIyMjAwDg4eGB9PR0iZerqyscHR3Z1U/m01Gry+41ABUrDgK55Sv0WtnXyXGwFISloGcFz+pk/7XB19eX5OTkCACNHj2aSkpKZB1Svau0OqVYXL5ssyxe1ay4Kk1+fj6pqalRQkICeXh40MqVK7ltf/31F2loaFBZWRkREcXExBAA+uGHH7g6EydOJE9PTyIievnyJY0cOZIMDAxIIBBQp06d6ODBg1zdffv2kZaWFhUXF0vE4O7uTmPGjCEiolGjRpGrqyu3LSgoiCwsLGjq1Knk4+PDlS9ZsoTatGnDvYeUlVePHTtGTk5OJBAIyNLSkq5evfrO/ggLC6OWLVtSSUkJGRgYUEREhMR2Hx8fsrKyqtTu7dVeDx8+TADoxIkTleqKxWLKycl5ZywfIigoiOTk5CgjI4Mr27lzJwmFQnr9+rXUNi9evCAAdPnyZa4sLy+PAFBISIjUNs+fPydFRUWJ1V4Z5k2yWLWXXRlporZs2YKJEydCLBZj8uTJ2L9/P1uHAwBevQLU1GTzquHl/yNHjsDMzAympqYYM2YM9u7dy4396d27N/Lz8xETEwMAuHTpEnR0dCSuXFy6dAlOTk4Aym+d2Nra4syZM7hz5w4mT54MLy8vXL9+HQAwfPhwiEQinDp1imv//PlznDlzBl9//TUAwNnZGVeuXOFuK4SGhsLJyQmOjo4Stw1CQ0Ph7Oxc7bktWrQIc+fORWxsLExMTDBq1Kh33q7w9fXFqFGjoKioiFGjRnG3gWoqICAApqamcHd3r7SNx+NBQ0OjyrZqamrVvqp7gnFkZCQ6d+6MFi1acGWurq7Iy8vD3bt3pbbR1taGqakp9u/fj8LCQpSVleG3336Drq4ubG1tpbbZv38/VFRUMGzYsCpjYZh6V6upTQNQ6cqIZXeikkdEZS9r7Rix6bGf7JURsVhMy5cv//8+AM2ZM4fENfyLvDGp9BdAQQFR+QN66/9VUFCj2O3t7Wnz5s1ERFRaWko6OjoUGhrKbbexsaF169YREdHQoUNp5cqVpKSkRPn5+fT48WMCQElJSVXuf9CgQTRnzhzu/dSpU+mzzz7j3m/YsIHatWvH/f7cu3ePAHBXMbp160ZHjhyhp0+fEp/Pp6KiInr16hXx+Xzat28ftx9IuTLy+++/c9vv3r1LACg+Pr7KWHNzc0kgEFBsbCwRlV8JUlNTo/z8fK7O+14ZMTc3p88//7zKY1Xn3r171b6ePav682DSpEk0YMAAibLCwkICQEFBQVW2e/ToEdna2hKPxyN5eXnS19en6OjoKuubm5vT1KlTa35yTJMhiysjTXOgxJtK7gD3DQH1YUDLox+9u+eFz2G7+7+/SBTkPq0uzszMxPbt2wEAy5Ytw08//QRebTzlrbFQUQEKCmR37PeUmJiI69ev4/jx4wAABQUFeHh4wNfXl7va4ejoiLCwMMyZMwfh4eFYvXo1jhw5gitXriArKwsGBgYwNjYGUD5eYdWqVThy5AiePHmCkpISvH79GipvxDRp0iR069YNT548QcuWLeHv74/x48dzvz8dOnRAq1atEBYWBgsLC8TExHDjElq3bo3IyEgQEV6/fv3OKyOWlpbcv/X1y5/T8/z5c5iZmUmtf+jQIbRv3x5WVlYAyld8bdOmDQ4fPoyJEye+d78CkjPLaqpDhw4f3PZDEBG8vb2hq6uL8PBwCAQC/P777xgyZAiioqK4vqsQGRmJ+Ph4HDhwoF7jZJh3+bS+KRuBF4UvICIRAOCnPj9BS6Al44gk6ejoICQkBJcuXYK3t7esw/n08HiA6qe/aKKvry/KyspgYGDAlRER+Hw+fv31V2hoaMDJyQl79+7FzZs3oaioCDMzMzg5OSEsLAzZ2dlwdHTk2q5btw5btmzB5s2b0blzZ6iqquK7775DSUkJV6dLly6wsrLC/v37MWDAANy9exdnzpyRiMvJyQmhoaGwtLSEsbExN0Cy4lYNEaFDhw4wNDSs9vzevGVYkeyIxeJq++Pu3btQeGMgulgsxt69e7lkRCgUIjc3t1LbnJwcAOBuv5iYmCAhIaHa+KqipqZW7fYxY8Zg165dUrfp6elxt8UqPHv2jNsmzcWLF3H69GlkZ2dDKBQCAHbs2IGQkBDs27cPCxYskKj/+++/w9rauspbOAwjKywZqWW5r8s/7HRUdPCz888yjqZcaWkpbt++DRsbGwBAp06d0KlTJxlHxXyosrIy7N+/Hxs2bMCAAQMktg0dOhSHDh3Ct99+y40b2bRpE5d4ODk5Yc2aNcjOzsacOXO4dhEREXB3d8eYMWMAlH+RJyUlVVqP6JtvvsHmzZvx5MkTuLi4VEoqnJ2dMXPmTHTs2JG7QgMAffr0wZ49e0BE77wqUlO3b9/Gv//+i7CwMGhp/Zf8Z2VlwcnJCQkJCdzYmsePH+PZs2cS4zKio6OhrKyM1q1bAwBGjx6NkSNH4uTJk5XGjRAR8vLyqhw38ub0YGkqEgZp7OzssHLlSjx//pxL4kJCQiAUCqtcF6pimrGcnOTwPzk5uUrJW0FBAY4cOYLVq1dXGyPDyESt3vRpACqNGTFTIYoH0eNhH73vO8/ucGNFdH7RqYVoP15xcTG5u7uTsrIyhYWFyTqcT05190Y/VcePHyclJSWpszrmz59PXbt25d5bW1uTvLw87dy5k4iIMjMzSVFRkQBQQkICV2/27NlkaGhIERERFBcXR9988w0JhUJyd3eX2H9OTg6pqKiQkpISBQYGVjr+gwcPCACpq6tLbH/48CEpKSmRkpKSxCwdIuljRirGbxARZWdnEwCJ8TBvmjVrFvXo0UPqtu7du9PcuXOJqHxcjYWFBTk7O1NERATdv3+fjh49Svr6+hKzjMRiMXl4eJBAIKCVK1dSVFQUpaam0l9//UV9+/blYq1tZWVl1KlTJxowYADFxsbS2bNnqXnz5vTjjz9yda5du0ampqb0+PFjIiqfTaOtrU1ffvklxcbGUmJiIs2dO5cUFRW58TMVfv/9d1JWVqbs7Ow6iZ9pPGQxZoQlI7WYjATeDuSSkVnBsz4+2I9UUFBA/fv3JwDE5/Ppr7/+knVIn5yGmIwMHjyYBg4cKHXbtWvXCADdvHmTiMq/qPHW4E8rKyvS09OTaJeZmUnu7u6kpqZGurq6tHjxYho7dmylZISIyMvLS+o03wpt2rQhAJSeni5RbmRkRADo6dOnEuUfk4y8fv2atLW16ZdffpEay9q1a0lXV5ebtv7kyRMaN24ctW7dmgQCAXXs2JHWrFlTaVq7SCSinTt3Urdu3UhFRYWEQiHZ2trSli1b6NWrV1KPVRtSU1Pps88+I4FAQDo6OjRnzhwqLS3ltoeGhhIASklJ4cqioqJowIABpKWlRerq6tSzZ0+pA17t7Oxo9OjRdRY703jIIhnhEX3EaK0G6L9LrLkAhCjoZALVS98CisaA+pCP2vfhO4cx8thIOBk5IXRc6Lsb1KGcnBwMGjQIV69ehaqqKk6dOsU9ZIr5T3FxMVJSUtC2bVv21Nn31K9fP1hYWGDr1q2yDoVhmDpQ3edixXdobm5utbcda4qNGZFvDmh9/8HNV4WvwuZ/NoNAKC4rfneDevDixQu4uroiJiYGmpqaCA4ORs+ePWUdFtPAZWdnIywsDGFhYdixY4esw2EYphFhychH8o/1x4tXLyTKOut2llE0wMuXL+Ho6Ij4+Hg0b94cf//9t9T1OBimprp06YLs7GysXbsWpqamsg6HYZhGhCUjteTAFwdgo28DBTkFGGsZyywOTU1NWFhYID8/H+fPn2dfGkytSU1NlXUIDMM0UiwZMTf/4KZbr23Fvax7AIC2mm3Rsbn06Xf1SUFBAQEBAXjx4gVatmwp63AYhmEY5p3Y2jS//fZBzUpFpfj+3H9jTXRVZbf6ZXR0NGbPns09V0BJSYklIgzDMEyDwa6MyMt/UDMCcU9aPTXyFIy1ZXNrJiIiAgMHDkReXh5atmyJuXPnyiQOhmEYhvlQ7MpILejTpo9MjhsSEoIBAwYgLy8PvXv3xuTJk2USB8MwDMN8DJaMNFAnTpzA4MGD8erVK7i6uuLs2bO1OuebYRiGYeoLS0ZSuwMpnYHiaFlH8t4CAgIwbNgwlJSU4KuvvsLJkyclVldlGIZhmIaEjRl5fReQfwWIX8k6kvfy9OlTfPPNNxCJRBg7dix8fX0lViplGIZhmIaGXRlpYAwMDBAQEIAZM2bAz8+PJSJN1Pjx48Hj8cDj8aCoqIgWLVqgf//+2Lt3b6XVWmXt0KFDkJeXh7e3d6Vt/v7+0NTUlNqOx+PhxIkTEmXHjh2Dk5MTNDQ0oKamBktLS/z888/Iysqqg8jLZWVlwdPTE0KhEJqampg4cSIKCgre2S4yMhJ9+/aFqqoqhEIh+vTpg6KiIm57UlIS3N3doaOjA6FQiF69eiE0VLbLSDCMrDTpZMSh+RWoCP7/ioi8jmyDqQYR4eXLl9z7L7/8Elu3bq20bDjTtLi5uSE9PR2pqakIDg6Gs7MzZs2ahcGDB6OsrKzKdqWlpfUYJeDr64v58+fj0KFDKC7+8CUTFi1aBA8PD3Tr1g3BwcG4c+cONmzYgJs3b+LAgQO1GLEkT09P3L17FyEhITh9+jQuX778zsHikZGRcHNzw4ABA3D9+nVERUVh+vTpEv/PVvycLl68iBs3bsDKygqDBw9GRkZGnZ0Lw3yyanXZvQagYsXBZGiTeKxi+Yq98SASi2u0n4LXBdwKvTlFlZdyry1isZhmzZpFhoaGlJqaWmfHaareXp1SLBZTwesCmbzENfgdHDdunNQVdS9cuEAAaM+ePVwZANqxYwcNGTKEVFRUyMfHh8rKyujrr78mIyMjUlZWJhMTE9q8eTPX5vbt28Tj8ej58+dEVL6qL4/HIw8PD67O8uXLycHBodo4Hzx4QAKBgHJycqhHjx4UEBAgsd3Pz480NDSktsUbq/lWrEb8Zoxvys7OrjaODxUXF0cAKCoqiisLDg4mHo9HT548qbJdjx49aPHixVVuf/HiBQGgy5cvc2V5eXkEgEJCQmoneIb5QLJYtbfJXuNXwSvwFJoBqjaA3m8Aj/febfNe56H91vZ1GF05kUiEKVOmwNfXFwBw6dIljB07ts6P25S9Kn0FtdVqMjl2wY8FUFVS/ah99O3bF1ZWVvjzzz/xzTffcOVLly7FmjVrsHnzZigoKEAsFqNVq1Y4evQotLW1cfXqVUyePBn6+voYMWIELCwsoK2tjUuXLmHYsGEIDw/n3le4dOkSnJycqo3Hz88PgwYNgoaGBsaMGQNfX1+MHj26xucVEBAANTU1TJs2Ter2qm71AICFhQUePnxY5fbevXsjODhY6rbIyEhoamqia9euXJmLiwvk5ORw7do1fPHFF5XaPH/+HNeuXYOnpyfs7e1x//59mJmZYeXKlejVqxcAQFtbG6ampti/fz9sbGzA5/Px22+/QVdXF7a2tlXGyjCNVZNNRgAA6qMAw801bpaclYyXr8pvmzi2cYSQX/tTaktKSuDl5YUjR45ATk4Oe/fuZYkI817MzMxw69YtibLRo0djwoQJEmXLli3j/t22bVtERkbiyJEjGDFiBHg8Hvr06YOwsDAMGzYMYWFhmDBhAn7//XckJCSgffv2uHr1KubPn19lHGKxGP7+/ti2bRsAYOTIkZgzZw63NHlN3Lt3D+3atYOiomKN2gFAUFBQtbemBAJBldsyMjKgqyv5dGUFBQVoaWlVeTvlwYMHAMoTwPXr18Pa2hr79+9Hv379cOfOHRgbG4PH4+H8+fMYOnQo1NXVIScnB11dXZw9exbNmjWr8TkyTEPXtJORGlwNkUZPTQ+h40LB+8j9vK2oqAjDhw/HmTNnoKioiEOHDuGrr76q1WMw0qkoqqDgx3cPTqyrY9cGIqr0O/nmX/YVtm/fjr179yItLQ1FRUUoKSmRWOHZ0dERu3fvBlB+FWTVqlVISkpCWFgYsrKyUFpaCgcHhyrjCAkJQWFhIQYOHAgA0NHR4QbZLl++vMbn9KHatGnzwW0/RMUA4ilTpnAJYJcuXXDhwgXs3bsXq1evBhHB29sburq6CA8Ph0AgwO+//44hQ4YgKioK+vr69Rozw8ha005GPpI8T77WE5H8/Hy4u7sjNDQUysrKOH78ONzc3Gr1GEzVeDzeR98qkbX4+PhKVx5UVSXPKTAwEHPnzsWGDRtgZ2cHdXV1rFu3DteuXePqODk54bvvvsO9e/cQFxeHXr16ISEhAWFhYcjOzkbXrl2rfb6Nr68vsrKyJK48iMVi3Lp1C8uWLYOcnByEQiEKCwshFoslBnfm5OQAADQ0NAAAJiYmuHLlCkpLS2t8deRjbtPo6enh+fPnEmVlZWXIysqCnp6e1DYViUTHjpILZ5qbmyMtLQ0AcPHiRZw+fRrZ2dncwwp37NiBkJAQ7Nu3DwsWLHi/k2OYRoIlI5+YsrIyvHz5Eurq6jh9+jT69JHNo+aZhunixYu4ffs2Zs+eXW29iIgI2NvbS4zBuH//vkSdzp07o1mzZlixYgWsra2hpqYGJycnrF27FtnZ2dWOF8nMzMTJkycRGBgICwsLrlwkEqFXr174+++/4ebmBlNTU5SVlSE2NhY2NjZcvejo8ocQmpiYACi/zbR161bs2LEDs2bNqnS8nJycKseNfMxtGjs7O+Tk5ODGjRvcWI6LFy9CLBajR48eUtsYGRnBwMAAiYmJEuVJSUn47LPPAACvXpXP4nt7RpycnNwnNzWbYepFrQ6HbQAqRgI/hYDou+8+aB83nt4gLAW13NCylqMrl5GRQf/++2+d7JuRVN2o8U/ZuHHjyM3NjdLT0+nx48d048YNWrlyJampqdHgwYOprKyMq4s3ZqVU2LJlCwmFQjp79iwlJibS4sWLSSgUkpWVlUS9oUOHkry8PP3www9ERCQSiahZs2YkLy9PZ8+erTK+TZs2kb6+vtQZQiNGjKBhw4Zx7wcMGEBWVlZ0/vx5evDgAQUHB5OpqanEzB0iovnz55O8vDzNmzePrl69SqmpqXT+/HkaNmxYlbNsaoObmxt16dKFrl27RleuXCFjY2MaNWoUt/3x48dkampK165d48o2bdpEQqGQjh49Svfu3aPFixeTsrIyJScnE1H5bBptbW368ssvKTY2lhITE2nu3LmkqKhIsbGxdXYuDPM+ZDGbhiUjH6C2k5G0tDTav39/reyLqZmGnIwAIACkoKBAzZs3JxcXF9q7dy+JRCKJutKSkeLiYho/fjxpaGiQpqYmTZ06lRYsWFApGdm0aRMBoODgYK7M3d2dFBQUKD8/v8r4OnfuTNOmTZO67fDhw6SkpEQvXrwgovJpuTNnzqT27duTQCAgY2Njmj9/vtT9Hz58mPr06UPq6uqkqqpKlpaW9PPPP9fZ1F6i8mnNo0aNIjU1NRIKhTRhwgSJ2FJSUggAhYaGSrRbvXo1tWrVilRUVMjOzo7Cw8MltkdFRdGAAQNIS0uL1NXVqWfPnhQUFFRn58Ew70sWyQiP6CNGhjVAeXl50NDQwFMIoP/dFGDTphrvIzo9Gra7bdFSvSUef//4o+JJTk5Gv379kJaWhkOHDmHkyJEftT+mZoqLi7nZHcrKyrIOh2EYRuaq+1ys+A7Nzc2t1cVZm/YjPHP3AdnbZXb4O3fuoHfv3khLS4OJiUm1MxMYhmEYprFq2smIKBsQZcrk0FFRUXB0dERGRgasrKwQHh4OQ0NDmcTCMAzDMLLUtJMRGbl8+TL69euHrKws9OzZE6GhoZUerMQwDMMwTQVLRupZSkoKXF1dkZ+fj759+yIkJIQ9cZFhGIZp0pr2c0Za1P8h27Ztizlz5uDmzZs4evQoGzTJMAzDNHlNNxn5Qh/4tgOgWPcL3gHlD3uSl5cHACxfvhwikQgKCk23+xmGYRimQtO9TTNwBdDuHKDhWeeH2rZtG/r168c9dZHH47FEhGEYhmH+X9NNRj6ASCziXu9r1apVmDlzJi5duoSDBw/WYXQMwzAM0zCxP8/f07Zr2zD73GyI6P0SESLCjz/+iLVr1wIAfHx8MHHixLoMkWEYhmEaJHZl5D2dvX+2UiLiZOQkta5YLMb06dO5RGT9+vVYunRpra/wyzCNjb+/f5UL3jEM03ixZKSGtn22DZnzM5E1PwsHvjhQaXtZWRnGjx+PHTt2gMfj4bfffsOcOXNkECnTmI0fPx5Dhw6tVB4WFgYej4ecnJx6j+lNCxYsgJmZmURZQkICeDwexo8fL1Hu7+8PPp+PoqIieHh4ICkpqdbiMDMzA5/PR0ZGRqVtRkZG2Lx5c6XypUuXwtraWqIsIyMDM2bMQLt27cDn82FoaIghQ4bgwoULtRarNEePHoWZmRmUlZXRuXNnBAUFvbNNQEAArKysoKKiAn19fXz99dfIzJR8uGNOTg68vb2hr68PPp8PExOT99o3w9QVloy8w/yQ+bDdbYvwh+EAADUlNWgJtNBM0EzqlY60tDScOXMG8vLy+N///ofJkyfXd8gMI3POzs5ITEyUSAJCQ0NhaGiIsLAwibqhoaHo2bMnBAIBBAJBrT0A8MqVKygqKsKwYcOwb9++D95PamoqbG1tcfHiRaxbtw63b9/G2bNn4ezsDG9v71qJVZqrV69i1KhRmDhxImJiYjB06FAMHToUd+7cqbJNREQExo4di4kTJ+Lu3bs4evQorl+/jkmTJnF1SkpK0L9/f6SmpuKPP/5AYmIi9uzZg5YtW9bZuTDMu7BkpBqFJYVYd3UdotOjkV+SDwBordG62jbt2rXDuXPn8Oeff2L06NH1ESZTi4iAwkLZvOpqycpjx47BwsICfD4fRkZG2LBhg8R2IyMjrFixAmPHjoWamhratGmDU6dO4cWLF3B3d4eamhosLS3x77//SrS7cuUKevfuDYFAAENDQ8ycOROFhYUAgF69ekFRUVEi8QgLC4O3tzeysrKQmpoqUe7s7Ayg8m2aiqsUBw4cgJGRETQ0NDBy5Ejk5+e/87x9fX0xevRoeHl5Ye/evTXstf9MmzYNPB4P169fx1dffQUTExNYWFjg+++/xz///PPB+32XLVu2wM3NDfPmzYO5uTmWL18OGxsb/Prrr1W2iYyMhJGREWbOnIm2bduiV69emDJlCq5fv87V2bt3L7KysnDixAk4ODjAyMgIjo6OsLKyqrNzYZh3YclINcQk5v59cuRJRE+OhrORc6V6eXl5Ev+zd+3aFZ9//nm9xMjUrlevADU12bz+f+Z3rbpx4wZGjBiBkSNH4vbt21i6dCl++ukn+Pv7S9TbtGkTHBwcEBMTg0GDBsHLywtjx47FmDFjEB0djfbt22Ps2LGoWOT7/v37cHNzw1dffYVbt27h8OHDuHLlCqZPnw4AUFVVRbdu3RAaGsodIywsDP369YODgwNX/uDBA6SlpXHJiDT379/HiRMncPr0aZw+fRqXLl3CmjVrqj3v/Px8HD16FGPGjEH//v2Rm5uL8PDwGvdfVlYWzp49C29vb6iqqlbaXt34loCAAKipqVX7qi6myMhIuLi4SJS5uroiMjKyyjZ2dnZ49OgRgoKCQER49uwZ/vjjDwwcOJCrc+rUKdjZ2cHb2xstWrRAp06dsGrVKohE7z9LkGFqW9OdTZMXCDyLApqvAuSUcf7BeRyLOyZRpURUwv17QPsBUFao/LTUzMxMuLq6IjExERcuXED37t3rPHSGAYDTp09DTU1NouztL5SNGzeiX79++OmnnwAAJiYmiIuLw7p16yTGbgwcOBBTpkwBACxZsgQ7d+5Et27dMHz4cADADz/8ADs7Ozx79gx6enpYvXo1PD098d133wEAjI2NsXXrVjg6OmLnzp1QVlaGs7Mzjh49CgCIi4tDcXExunTpgj59+iAsLAwTJkxAWFgYlJWV0bNnzyrPUywWw9/fH+rq6gAALy8vXLhwAStXrqyyTWBgIIyNjWFhYQEAGDlyJHx9fdG7d+93dauE5ORkEFGl8S/v4/PPP0ePHj2qrVPdrZGMjAy0aCH5mOgWLVpIHf9SwcHBAQEBAfDw8EBxcTHKysowZMgQbN/+3+rkDx48wMWLF+Hp6YmgoCAkJydj2rRpKC0thY+Pz3ueHcPUrqabjBSGANmnAJ2lAJQx+a/JSMlJkVpVVVEVCnKVuyo9PR39+/fH3bt3oaOjwx5k1gioqAAFBbI7dk04Oztj586dEmXXrl3DmDFjuPfx8fFwd3eXqOPg4IDNmzdLPBXY0tKS217xBdi5c+dKZc+fP4eenh5u3ryJW7duISAggKtDRBCLxUhJSYG5uTmcnJywcuVKpKenIywsDL169YK8vDwcHR2xa9cuAOVXS+zt7cHn86s8TyMjIy4RAQB9fX08f/682r7Zu3evRD+MGTMGjo6O2LZtm8S+3oU+4t6Zurp6jY5VG+Li4jBr1iwsWbIErq6uSE9Px7x58/Dtt9/C19cXQHlyp6uri927d0NeXh62trZ48uQJ1q1bx5IRRmbYt+f/e1Vafo18WtdpaKEm+ddI79a9KyUjqampcHFxwf3799GyZUuEhITA3Ny83uJl6gaPB0i5Gv9JUlVVRYcOHSTKHj9+/EH7UlRU5P5dMTBbWplYXH7rsqCgAFOmTMHMmTMr7at16/JxVQ4ODlBSUkJoaChCQ0Ph6OgIAOjWrRtevnyJBw8eICwsjLsi8z6xVcRSEYc0cXFx+Oeff3D9+nX88MMPXLlIJEJgYCA3mFMoFCI3N7dS+5ycHGhoaAAov+LD4/GQkJBQbYzSBAQEvPPcgoODq7xao6enh2fPnkmUVVyZqsrq1avh4OCAefPmAShPMlVVVdG7d2+sWLEC+vr60NfXh6KiIpeIAoC5uTkyMjJQUlICJSWl9z1Fhqk1TT4ZCUsNR0recy4Z+bbrt+jconO1bRISEuDi4oInT56gbdu2uHDhAtq2bVsf4TJMjZibmyMiIkKiLCIiAiYmJhJfRjVlY2ODuLi4SsnQmwQCAXr06IGwsDBcunSJ+4JUVFREz5494evri0ePHlU7XuRD+Pr6ok+fPhK3JgDAz88Pvr6+XDJiamqKGzduVGofHR0NU1NTAICWlhZcXV2xfft2zJw5s9K4kZycnCrHjXzsbRo7OztcuHCBuxUGACEhIbCzs6uyzatXrypdoa34OVdc5XFwcMDBgwchFoshJ1c+bDApKQn6+vosEWFkh5qY3NxcAkBPfxZQ3DUQlkq+El4kVNv+3r171Lx5cwJA5ubm9Pjx43qKnKkLRUVFFBcXR0VFRbIOpUbGjRtH7u7ulcpDQ0MJAGVnZxMR0Y0bN0hOTo5+/vlnSkxMJH9/fxIIBOTn58e1adOmDW3atEliPwDo+PHj3PuUlBQCQDExMUREdPPmTRIIBOTt7U0xMTGUlJREJ06cIG9vb4n9LFmyhNTV1UldXZ1KS0u58mXLlpG6ujqpqqpSSUkJV+7n50caGhrcex8fH7KyspLY56ZNm6hNmzZS+6WkpISaN29OO3furLQtLi6OANCdO3eIiCgiIoLk5ORoxYoVFBcXR7dv36aFCxeSgoIC3b59m2t3//590tPTo44dO9Iff/xBSUlJFBcXR1u2bCEzMzOpcdSGiIgIUlBQoPXr11N8fDz5+PiQoqKiRGwLFiwgLy8v7r2fnx8pKCjQjh076P79+3TlyhXq2rUrde/enauTlpZG6urqNH36dEpMTKTTp0+Trq4urVixos7OhWlYqvtcrPgOzc3NrdVjNunZNM+Ky/+rqqiKgcYDMd9+Pky0Tapt07p1a3Tr1g02Nja4fPkym5vPfNJsbGxw5MgRBAYGolOnTliyZAl+/vnnSg8eqylLS0tcunQJSUlJ6N27N7p06YIlS5bAwMBAop6zszPy8/Ph4OAg8Re7o6Mj8vPzuSnAteXUqVPIzMzEF198UWmbubk5zM3NubET9vb2CA4ORnBwMBwcHODk5ISrV6/iwoUL6NSpE9euXbt2iI6OhrOzM+bMmYNOnTqhf//+uHDhQqUxO7XJ3t4eBw8exO7du2FlZYU//vgDJ06ckIgtPT0daWlp3Pvx48dj48aN+PXXX9GpUycMHz4cpqam+PPPP7k6hoaGOHfuHKKiomBpaYmZM2di1qxZWLBgQZ2dC8O8C4+orp5u8GnKy8uDhoYGLv76C1LtdfD1qa/RsXlH3J129733UVRUhJKSEu6+MtNwFRcXIyUlBW3btoWycuXZUgzDME1NdZ+LFd+hubm5EAqFtXbMJjtmpG+KD/Cy6L3q/vXXXwgNDcWGDRvA4/G4J0UyDMMwDPPxmmwyoiqnDIGKKuR4chhnNa7KeocOHYKXlxdEIhFsbGwkpgsyDMMwDPPxmmwysln/a3wze321dfbs2YMpU6aAiDBmzBiMHDmynqJjGIZhmKajSQ9grc7GjRsxefJkEBG+/fZb7Nu3jz3UjGEYhmHqAEtG3kJEWLp0KebMmQMAmDdvHnbs2MHNx2capyY2jpthGKZKsvg8ZN+wb7lz5w5WrFgBAFixYgXWrl3LPX2SaXwqHghVUlLyjpoMwzBNQ8Xn4cc8GLGm2H2Ht3Tu3Bl+fn7IysrCrFmzZB0OU8cUFBSgoqKCFy9eQFFRkV0BYximSROLxXjx4gVUVFTqdWgCS0YAlJaWIjMzk1vzwcvLS8YRMfWFx+NBX18fKSkpePjwoazDYRiGkTk5OTm0bt26Xu8KNN1kJDcQeBCMYr1LGDHya8THx+Py5cvQ19eXdWRMPVNSUoKxsTG7VcMwDIPyz8T6vkrcdJMRcRYKsp9g6KRhuHDxEpSVlREfH8+SkSZKTk6OPYGVYRhGRj6JG+Tbt2+HkZERlJWV0aNHD1y/fr3a+kePHoWZmRmUlZXRuXNnBAUF1fiYr4oJA74BLly8BDU1NQQHB6Nv374fegoMwzAMw3wgmScjhw8fxvfffw8fHx9ER0fDysoKrq6ueP78udT6V69exahRozBx4kTExMRg6NChGDp0KO7cuVOj467fX4zIWKBZM02cP38eTk5OH38yDMMwDMPUmMwXyuvRowe6deuGX3/9FUD5SF5DQ0PMmDFD6iqSHh4eKCwsxOnTp7mynj17wtraGrt27Xrn8SoW+QGAFjrA339HwLKLfS2dDcMwDMM0Xo1yobySkhLcuHEDP/74I1cmJycHFxcXREZGSm0TGRmJ77//XqLM1dUVJ06ckFr/9evXeP36Nfc+NzcXAKCpLoegfd1g1LYt8vLyPvJMGIZhGKbxq/i+rO3rGDJNRl6+fAmRSIQWLVpIlLdo0QIJCQlS22RkZEitn5GRIbX+6tWrsWzZskrlOfli2A66BsDgw4JnGIZhmCYqMzOTu8tQGxr9bJoff/xR4kpKTk4O2rRpg7S0tFrtSKZqeXl5MDQ0xKNHj2r1sh5TNdbn9Y/1ef1jfV7/cnNz0bp1a2hpadXqfmWajOjo6EBeXh7Pnj2TKH/27Bn3ALK36enp1ag+n88Hn8+vVK6hocF+eeuZUChkfV7PWJ/XP9bn9Y/1ef2r7eeQyHQ2jZKSEmxtbXHhwgWuTCwW48KFC7Czs5Paxs7OTqI+AISEhFRZn2EYhmGYT5vMb9N8//33GDduHLp27Yru3btj8+bNKCwsxIQJEwAAY8eORcuWLbF69WoAwKxZs+Do6IgNGzZg0KBBCAwMxL///ovdu3fL8jQYhmEYhvlAMk9GPDw88OLFCyxZsgQZGRmwtrbG2bNnuUGqaWlpEpeD7O3tcfDgQSxevBgLFy6EsbExTpw4gU6dOr3X8fh8Pnx8fKTeumHqBuvz+sf6vP6xPq9/rM/rX131ucyfM8IwDMMwTNMm8yewMgzDMAzTtLFkhGEYhmEYmWLJCMMwDMMwMsWSEYZhGIZhZKpRJiPbt2+HkZERlJWV0aNHD1y/fr3a+kePHoWZmRmUlZXRuXNnBAUF1VOkjUdN+nzPnj3o3bs3mjVrhmbNmsHFxeWdPyOmspr+nlcIDAwEj8fD0KFD6zbARqimfZ6TkwNvb2/o6+uDz+fDxMSEfb7UUE37fPPmzTA1NYVAIIChoSFmz56N4uLieoq24bt8+TKGDBkCAwMD8Hi8Ktd9e1NYWBhsbGzA5/PRoUMH+Pv71/zA1MgEBgaSkpIS7d27l+7evUuTJk0iTU1NevbsmdT6ERERJC8vT7/88gvFxcXR4sWLSVFRkW7fvl3PkTdcNe3z0aNH0/bt2ykmJobi4+Np/PjxpKGhQY8fP67nyBuumvZ5hZSUFGrZsiX17t2b3N3d6yfYRqKmff769Wvq2rUrDRw4kK5cuUIpKSkUFhZGsbGx9Rx5w1XTPg8ICCA+n08BAQGUkpJC586dI319fZo9e3Y9R95wBQUF0aJFi+jPP/8kAHT8+PFq6z948IBUVFTo+++/p7i4ONq2bRvJy8vT2bNna3TcRpeMdO/enby9vbn3IpGIDAwMaPXq1VLrjxgxggYNGiRR1qNHD5oyZUqdxtmY1LTP31ZWVkbq6uq0b9++ugqx0fmQPi8rKyN7e3v6/fffady4cSwZqaGa9vnOnTupXbt2VFJSUl8hNjo17XNvb2/q27evRNn3339PDg4OdRpnY/U+ycj8+fPJwsJCoszDw4NcXV1rdKxGdZumpKQEN27cgIuLC1cmJycHFxcXREZGSm0TGRkpUR8AXF1dq6zPSPqQPn/bq1evUFpaWusLLzVWH9rnP//8M3R1dTFx4sT6CLNR+ZA+P3XqFOzs7ODt7Y0WLVqgU6dOWLVqFUQiUX2F3aB9SJ/b29vjxo0b3K2cBw8eICgoCAMHDqyXmJui2voOlfkTWGvTy5cvIRKJuKe3VmjRogUSEhKktsnIyJBaPyMjo87ibEw+pM/f9sMPP8DAwKDSLzQj3Yf0+ZUrV+Dr64vY2Nh6iLDx+ZA+f/DgAS5evAhPT08EBQUhOTkZ06ZNQ2lpKXx8fOoj7AbtQ/p89OjRePnyJXr16gUiQllZGb799lssXLiwPkJukqr6Ds3Ly0NRUREEAsF77adRXRlhGp41a9YgMDAQx48fh7KysqzDaZTy8/Ph5eWFPXv2QEdHR9bhNBlisRi6urrYvXs3bG1t4eHhgUWLFmHXrl2yDq3RCgsLw6pVq7Bjxw5ER0fjzz//xJkzZ7B8+XJZh8a8Q6O6MqKjowN5eXk8e/ZMovzZs2fQ09OT2kZPT69G9RlJH9LnFdavX481a9bg/PnzsLS0rMswG5Wa9vn9+/eRmpqKIUOGcGVisRgAoKCggMTERLRv375ug27gPuT3XF9fH4qKipCXl+fKzM3NkZGRgZKSEigpKdVpzA3dh/T5Tz/9BC8vL3zzzTcAgM6dO6OwsBCTJ0/GokWLan3Ze6bq71ChUPjeV0WARnZlRElJCba2trhw4QJXJhaLceHCBdjZ2UltY2dnJ1EfAEJCQqqsz0j6kD4HgF9++QXLly/H2bNn0bVr1/oItdGoaZ+bmZnh9u3biI2N5V6ff/45nJ2dERsbC0NDw/oMv0H6kN9zBwcHJCcnc4kfACQlJUFfX58lIu/hQ/r81atXlRKOimSQ2DJsdaLWvkNrNrb20xcYGEh8Pp/8/f0pLi6OJk+eTJqampSRkUFERF5eXrRgwQKufkREBCkoKND69espPj6efHx82NTeGqppn69Zs4aUlJTojz/+oPT0dO6Vn58vq1NocGra529js2lqrqZ9npaWRurq6jR9+nRKTEyk06dPk66uLq1YsUJWp9Dg1LTPfXx8SF1dnQ4dOkQPHjygv//+m9q3b08jRoyQ1Sk0OPn5+RQTE0MxMTEEgDZu3EgxMTH08OFDIiJasGABeXl5cfUrpvbOmzeP4uPjafv27Wxqb4Vt27ZR69atSUlJibp3707//PMPt83R0ZHGjRsnUf/IkSNkYmJCSkpKZGFhQWfOnKnniBu+mvR5mzZtCECll4+PT/0H3oDV9Pf8TSwZ+TA17fOrV69Sjx49iM/nU7t27WjlypVUVlZWz1E3bDXp89LSUlq6dCm1b9+elJWVydDQkKZNm0bZ2dn1H3gDFRoaKvXzuaKfx40bR46OjpXaWFtbk5KSErVr1478/PxqfFweEbt2xTAMwzCM7DSqMSMMwzAMwzQ8LBlhGIZhGEamWDLCMAzDMIxMsWSEYRiGYRiZYskIwzAMwzAyxZIRhmEYhmFkiiUjDMMwDMPIFEtGGIZhGIaRKZaMMEwj4+/vD01NTVmH8cF4PB5OnDhRbZ3x48dj6NCh9RIPwzB1jyUjDPMJGj9+PHg8XqVXcnKyrEODv78/F4+cnBxatWqFCRMm4Pnz57Wy//T0dHz22WcAgNTUVPB4PMTGxkrU2bJlC/z9/WvleFVZunQpd57y8vIwNDTE5MmTkZWVVaP9sMSJYd5NQdYBMAwjnZubG/z8/CTKmjdvLqNoJAmFQiQmJkIsFuPmzZuYMGECnj59inPnzn30vqtaHv5NGhoaH32c92FhYYHz589DJBIhPj4eX3/9NXJzc3H48OF6OT7DNBXsygjDfKL4fD709PQkXvLy8ti4cSM6d+4MVVVVGBoaYtq0aSgoKKhyPzdv3oSzszPU1dUhFApha2uLf//9l9t+5coV9O7dGwKBAIaGhpg5cyYKCwurjY3H40FPTw8GBgb47LPPMHPmTJw/fx5FRUUQi8X4+eef0apVK/D5fFhbW+Ps2bNc25KSEkyfPh36+vpQVlZGmzZtsHr1aol9V9ymadu2LQCgS5cu4PF4cHJyAiB5tWH37t0wMDCAWCyWiNHd3R1ff/019/7kyZOwsbGBsrIy2rVrh2XLlqGsrKza81RQUICenh5atmwJFxcXDB8+HCEhIdx2kUiEiRMnom3bthAIBDA1NcWWLVu47UuXLsW+fftw8uRJ7ipLWFgYAODRo0cYMWIENDU1oaWlBXd3d6SmplYbD8M0ViwZYZgGRk5ODlu3bsXdu3exb98+XLx4EfPnz6+yvqenJ1q1aoWoqCjcuHEDCxYsgKKiIgDg/v37cHNzw1dffYVbt27h8OHDuHLlCqZPn16jmAQCAcRiMcrKyrBlyxZs2LAB69evx61bt+Dq6orPP/8c9+7dAwBs3boVp06dwpEjR5CYmIiAgAAYGRlJ3e/169cBAOfPn0d6ejr+/PPPSnWGDx+OzMxMhIaGcmVZWVk4e/YsPD09AQDh4eEYO3YsZs2ahbi4OPz222/w9/fHypUr3/scU1NTce7cOSgpKXFlYrEYrVq1wtGjRxEXF4clS5Zg4cKFOHLkCABg7ty5GDFiBNzc3JCeno709HTY29ujtLQUrq6uUFdXR3h4OCIiIqCmpgY3NzeUlJS8d0wM02h87HLDDMPUvnHjxpG8vDypqqpyr2HDhkmte/ToUdLW1ube+/n5kYaGBvdeXV2d/P39pbadOHEiTZ48WaIsPDyc5OTkqKioSGqbt/eflJREJiYm1LVrVyIiMjAwoJUrV0q06datG02bNo2IiGbMmEF9+/YlsVgsdf8A6Pjx40RElJKSQgAoJiZGos64cePI3d2de+/u7k5ff/019/63334jAwMDEolERETUr18/WrVqlcQ+Dhw4QPr6+lJjICLy8fEhOTk5UlVVJWVlZW4p9Y0bN1bZhojI29ubvvrqqypjrTi2qampRB+8fv2aBAIBnTt3rtr9M0xjxMaMMMwnytnZGTt37uTeq6qqAii/SrB69WokJCQgLy8PZWVlKC4uxqtXr6CiolJpP99//z2++eYbHDhwgLvV0L59ewDlt3Bu3bqFgIAArj4RQSwWIyUlBebm5lJjy83NhZqaGsRiMYqLi9GrVy/8/vvvyMvLw9OnT+Hg4CBR38HBATdv3gRQfoulf//+MDU1hZubGwYPHowBAwZ8VF95enpi0qRJ2LFjB/h8PgICAjBy5EjIyclx5xkRESFxJUQkElXbbwBgamqKU6dOobi4GP/73/8QGxuLGTNmSNTZvn079u7di7S0NBQVFaGkpATW1tbVxnvz5k0kJydDXV1dory4uBj379//gB5gmIaNJSMM84lSVVVFhw4dJMpSU1MxePBgTJ06FStXroSWlhauXLmCiRMnoqSkROqX6tKlSzF69GicOXMGwcHB8PHxQWBgIL744gsUFBRgypQpmDlzZqV2rVu3rjI2dXV1REdHQ05ODvr6+hAIBACAvLy8d56XjY0NUlJSEBwcjPPnz2PEiBFwcXHBH3/88c62VRkyZAiICGfOnEG3bt0QHh6OTZs2cdsLCgqwbNkyfPnll5XaKisrV7lfJSUl7mewZs0aDBo0CMuWLcPy5csBAIGBgZg7dy42bNgAOzs7qKurY926dbh27Vq18RYUFMDW1lYiCazwqQxSZpj6xJIRhmlAbty4AbFYjA0bNnB/9VeMT6iOiYkJTExMMHv2bIwaNQp+fn744osvYGNjg7i4uEpJz7vIyclJbSMUCmFgYICIiAg4Ojpy5REREejevbtEPQ8PD3h4eGDYsGFwc3NDVlYWtLS0JPZXMT5DJBJVG4+ysjK+/PJLBAQEIDk5GaamprCxseG229jYIDExscbn+bbFixejb9++mDp1Knee9vb2mDZtGlfn7SsbSkpKleK3sbHB4cOHoaurC6FQ+FExMUxjwAawMkwD0qFDB5SWlmLbtm148OABDhw4gF27dlVZv6ioCNOnT0dYWBgePnyIiIgIREVFcbdffvjhB1y9ehXTp09HbGws7t27h5MnT9Z4AOub5s2bh7Vr1+Lw4cNITEzEggULEBsbi1mzZgEANm7ciEOHDiEhIQFJSUk4evQo9PT0pD6oTVdXFwKBAGfPnsWzZ8+Qm5tb5XE9PT1x5swZ7N27lxu4WmHJkiXYv38/li1bhrt37yI+Ph6BgYFYvHhxjc7Nzs4OlpaWWLVqFQDA2NgY//77L86dO4ekpCT89NNPiIqKkmhjZGSEW7duITExES9fvkRpaSk8PT2ho6MDd3d3hIeHIyUlBWFhYZg5cyYeP35co5gYplGQ9aAVhmEqkzboscLGjRtJX1+fBAIBubq60v79+wkAZWdnE5HkANPXr1/TyJEjydDQkJSUlMjAwICmT58uMTj1+vXr1L9/f1JTUyNVVVWytLSsNAD1TW8PYH2bSCSipUuXUsuWLUlRUZGsrKwoODiY2757926ytrYmVVVVEgqF1K9fP4qOjua2440BrEREe/bsIUNDQ5KTkyNHR8cq+0ckEpG+vj4BoPv371eK6+zZs2Rvb08CgYCEQiF1796ddu/eXeV5+Pj4kJWVVaXyQ4cOEZ/Pp7S0NCouLqbx48eThoYGaWpq0tSpU2nBggUS7Z4/f871LwAKDQ0lIqL09HQaO3Ys6ejoEJ/Pp3bt2tGkSZMoNze3ypgYprHiERHJNh1iGIZhGKYpY7dpGIZhGIaRKZaMMAzDMAwjUywZYRiGYRhGplgywjAMwzCMTLFkhGEYhmEYmWLJCMMwDMMwMsWSEYZhGIZhZIolIwzDMAzDyBRLRhiGYRiGkSmWjDAMwzAMI1MsGWEYhmEYRqb+Dxnoscokchl4AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "helperFunctions.plotROCcurve(yTrue_scores = vld_yTrue_scores, yPred_scores = vld_yPred_scores, classesInOrder = gridSearcher.classes_, classNumToName={'A':\"AwayWin\", 'D':\"Draw\", 'H':\"HomeWin\"}, title=\"ROC curve for Random Forests Model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average precision score, micro-averaged over all classes: 0.72\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "helperFunctions.plotPRcurve(yTrue_scores = vld_yTrue_scores, yPred_scores = vld_yPred_scores, classesInOrder = gridSearcher.classes_, classNumToName={'A':\"AwayWin\", 'D':\"Draw\", 'H':\"HomeWin\"}, title=\"ROC curve for Random Forests Model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DF</th>\n",
       "      <th>DST</th>\n",
       "      <th>DY</th>\n",
       "      <th>DR</th>\n",
       "      <th>DPr5MW</th>\n",
       "      <th>DPr5MD</th>\n",
       "      <th>DPr5ML</th>\n",
       "      <th>HTDG</th>\n",
       "      <th>RstDayDiff</th>\n",
       "      <th>B365DstDrw</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>-2</td>\n",
       "      <td>-1</td>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-2.314815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-3</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>-2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.619048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>-2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5.327869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-6</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1.409091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1.945946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>663</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>-8</td>\n",
       "      <td>-1.294118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>664</th>\n",
       "      <td>-2</td>\n",
       "      <td>0</td>\n",
       "      <td>-3</td>\n",
       "      <td>1</td>\n",
       "      <td>-3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>665</th>\n",
       "      <td>-6</td>\n",
       "      <td>-2</td>\n",
       "      <td>-2</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-2</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.891892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>666</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.306122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>667</th>\n",
       "      <td>-3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.254753</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>668 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     DF  DST  DY  DR  DPr5MW  DPr5MD  DPr5ML  HTDG  RstDayDiff  B365DstDrw\n",
       "0     6    0   2   0      -2      -1       3    -1          -1   -2.314815\n",
       "1    -3    6   2   1      -2       1       1     0           0    1.619048\n",
       "2     1    9   0   0       2       0      -2     1           2    5.327869\n",
       "3    -6   -1  -1   0      -1       1       0     0          -1   -1.409091\n",
       "4     4    0   2   0       0       0       0     1          -1    1.945946\n",
       "..   ..  ...  ..  ..     ...     ...     ...   ...         ...         ...\n",
       "663   2    0   1   0       0       1      -1     0          -8   -1.294118\n",
       "664  -2    0  -3   1      -3       0       3    -1          -1   -3.000000\n",
       "665  -6   -2  -2   0      -1       0       1    -2           0   -1.891892\n",
       "666   3    2   6   0       0       0       0     0           0    1.306122\n",
       "667  -3    0   2   0       1       0      -1     0           0    1.254753\n",
       "\n",
       "[668 rows x 10 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "og_test_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "og_test_y = gridSearcher.predict(og_test_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.exists('predictions'):\n",
    "    os.mkdir('predictions')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('predictions/randomForests.txt', 'w') as fh:\n",
    "    for prediction in og_test_y:\n",
    "        fh.write(prediction+\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['A', 'D', 'H', 'A', 'H', 'H', 'D', 'D', 'D', 'H', 'D', 'A', 'H',\n",
       "       'D', 'D', 'A', 'A', 'D', 'D', 'A', 'A', 'H', 'A', 'H', 'H', 'D',\n",
       "       'D', 'A', 'D', 'A', 'D', 'H', 'H', 'D', 'D', 'D', 'A', 'H', 'H',\n",
       "       'H', 'D', 'A', 'D', 'H', 'H', 'A', 'D', 'H', 'D', 'H', 'H', 'A',\n",
       "       'H', 'D', 'H', 'H', 'A', 'H', 'H', 'A', 'H', 'H', 'A', 'D', 'A',\n",
       "       'A', 'H', 'D', 'A', 'A', 'D', 'A', 'H', 'A', 'D', 'H', 'A', 'A',\n",
       "       'D', 'H', 'A', 'D', 'H', 'A', 'D', 'D', 'H', 'A', 'H', 'D', 'D',\n",
       "       'H', 'H', 'D', 'D', 'D', 'A', 'A', 'D', 'A', 'D', 'H', 'H', 'D',\n",
       "       'H', 'H', 'H', 'H', 'H', 'H', 'H', 'D', 'H', 'A', 'A', 'D', 'H',\n",
       "       'H', 'A', 'H', 'D', 'A', 'D', 'A', 'A', 'D', 'A', 'A', 'H', 'A',\n",
       "       'A', 'H', 'D', 'H', 'D', 'H', 'H', 'D', 'A', 'H', 'D', 'A', 'H',\n",
       "       'A', 'H', 'A', 'D', 'A', 'D', 'A', 'A', 'A', 'D', 'H', 'H', 'D',\n",
       "       'D', 'H', 'A', 'H', 'A', 'A', 'A', 'A', 'H', 'D', 'D', 'A', 'H',\n",
       "       'H', 'D', 'D', 'D', 'H', 'A', 'A', 'A', 'A', 'H', 'H', 'D', 'A',\n",
       "       'D', 'D', 'A', 'H', 'H', 'A', 'A', 'D', 'A', 'D', 'H', 'D', 'A',\n",
       "       'D', 'H', 'H', 'H', 'D', 'D', 'A', 'A', 'D', 'D', 'D', 'D', 'H',\n",
       "       'D', 'H', 'D', 'A', 'H', 'H', 'A', 'D', 'D', 'H', 'D', 'D', 'A',\n",
       "       'A', 'A', 'D', 'A', 'H', 'H', 'A', 'H', 'A', 'A', 'A', 'D', 'H',\n",
       "       'H', 'H', 'D', 'D', 'D', 'D', 'A', 'H', 'D', 'A', 'D', 'D', 'A',\n",
       "       'D', 'H', 'A', 'A', 'D', 'D', 'H', 'H', 'D', 'A', 'H', 'H', 'D',\n",
       "       'H', 'H', 'A', 'H', 'H', 'D', 'H', 'H', 'D', 'H', 'H', 'A', 'H',\n",
       "       'A', 'D', 'H', 'H', 'H', 'A', 'H', 'A', 'H', 'H', 'H', 'D', 'D',\n",
       "       'H', 'H', 'D', 'D', 'D', 'A', 'H', 'A', 'A', 'D', 'A', 'D', 'H',\n",
       "       'A', 'A', 'H', 'A', 'H', 'H', 'D', 'H', 'H', 'A', 'A', 'D', 'H',\n",
       "       'H', 'A', 'A', 'H', 'A', 'D', 'A', 'H', 'A', 'D', 'A', 'A', 'D',\n",
       "       'H', 'A', 'A', 'A', 'A', 'A', 'D', 'H', 'D', 'A', 'D', 'H', 'D',\n",
       "       'D', 'A', 'A', 'D', 'A', 'D', 'H', 'A', 'A', 'A', 'D', 'A', 'H',\n",
       "       'A', 'H', 'H', 'H', 'H', 'A', 'A', 'A', 'A', 'D', 'H', 'D', 'A',\n",
       "       'D', 'A', 'H', 'D', 'H', 'A', 'D', 'H', 'A', 'A', 'D', 'H', 'H',\n",
       "       'A', 'D', 'H', 'D', 'A', 'H', 'H', 'D', 'D', 'D', 'D', 'H', 'H',\n",
       "       'D', 'D', 'D', 'D', 'D', 'A', 'A', 'H', 'A', 'A', 'A', 'H', 'H',\n",
       "       'D', 'H', 'H', 'H', 'D', 'A', 'A', 'H', 'H', 'H', 'D', 'D', 'H',\n",
       "       'H', 'A', 'H', 'A', 'D', 'H', 'H', 'D', 'A', 'H', 'D', 'H', 'H',\n",
       "       'D', 'D', 'H', 'D', 'H', 'D', 'H', 'A', 'A', 'D', 'A', 'A', 'H',\n",
       "       'A', 'H', 'H', 'D', 'H', 'A', 'A', 'H', 'H', 'H', 'A', 'D', 'A',\n",
       "       'H', 'D', 'H', 'D', 'A', 'H', 'D', 'A', 'H', 'D', 'D', 'A', 'H',\n",
       "       'A', 'D', 'D', 'D', 'H', 'H', 'A', 'A', 'H', 'D', 'H', 'A', 'D',\n",
       "       'D', 'A', 'A', 'D', 'H', 'D', 'H', 'D', 'H', 'H', 'H', 'H', 'D',\n",
       "       'D', 'H', 'D', 'A', 'H', 'A', 'D', 'H', 'A', 'D', 'D', 'H', 'H',\n",
       "       'H', 'H', 'D', 'A', 'D', 'H', 'D', 'A', 'H', 'D', 'D', 'A', 'H',\n",
       "       'D', 'H', 'D', 'H', 'D', 'D', 'A', 'A', 'H', 'H', 'D', 'A', 'D',\n",
       "       'D', 'H', 'D', 'D', 'H', 'A', 'H', 'A', 'D', 'D', 'A', 'H', 'H',\n",
       "       'H', 'D', 'D', 'A', 'H', 'A', 'H', 'A', 'A', 'A', 'D', 'H', 'H',\n",
       "       'A', 'H', 'D', 'H', 'A', 'H', 'D', 'H', 'H', 'D', 'D', 'D', 'D',\n",
       "       'D', 'A', 'D', 'D', 'D', 'A', 'A', 'D', 'D', 'H', 'A', 'H', 'A',\n",
       "       'H', 'H', 'A', 'H', 'A', 'H', 'A', 'D', 'D', 'D', 'H', 'D', 'D',\n",
       "       'H', 'A', 'D', 'A', 'H', 'H', 'D', 'D', 'D', 'A', 'A', 'H', 'H',\n",
       "       'H', 'A', 'A', 'A', 'H', 'D', 'A', 'D', 'A', 'D', 'H', 'D', 'D',\n",
       "       'A', 'A', 'H', 'D', 'A', 'D', 'A', 'A', 'D', 'A', 'D', 'D', 'D',\n",
       "       'H', 'H', 'A', 'D', 'H', 'D', 'A', 'A', 'H', 'A', 'H', 'A', 'D',\n",
       "       'A', 'A', 'A', 'A', 'D', 'H', 'A', 'H', 'D', 'H', 'A', 'D', 'A',\n",
       "       'D', 'A', 'A', 'D', 'D'], dtype=object)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "og_test_y"
   ]
  }
 ],
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